Systems and techniques for estimating the severity of chronic obstructive pulmonary disease in a patient

ABSTRACT

Disclosed herein are embodiments of systems and techniques for estimating the severity of chronic obstructive pulmonary disease (COPD) in a patient. For example, in some embodiments, a system for estimating COPD severity in a patient may include logic to receive a breathing signal representative of breathing activity of the patient over a time interval, receive a locomotion signal representative of locomotive activity of the patient over the time interval, and provide breathing data and locomotion data to additional logic, wherein the additional logic is to generate an estimate of COPD severity in the patient by comparison of 1) a cross-recurrence quantification analysis (cRQA) parameter between the breathing data and the locomotion data and 2) a reference value. The breathing data may be based on the breathing signal, and the locomotion data may be based on the locomotion signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/034,396, filed on Aug. 7, 2014, and titled “SYSTEMS AND METHODS USINGBIORHYTHMS,” and U.S. Provisional Application No. 62/042,465, filed Aug.27, 2014, and titled “MONITORING SYSTEM FOR COPD.” Both of theseapplications are incorporated by reference herein in their entireties.

TECHNICAL FIELD

The present disclosure relates generally to the field of diagnosticsystems and, more particularly, to systems and techniques for estimatingthe severity of chronic obstructive pulmonary disease in a patient.

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic lung diseasethat causes obstructed airflow from the lungs. Existing techniques forclassifying the severity of COPD in a patient involve lung functiontesting, spirometry, symptom questionnaires, body mass measurements, andexercise capacity measurements, among others.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be readily understood by the following detaileddescription in conjunction with the accompanying drawings. To facilitatethis description, like reference numerals designate like structuralelements. Embodiments are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram of a computing system that may be configuredfor estimating the severity of chronic obstructive pulmonary disease(COPD) in a patient, in accordance with various embodiments.

FIG. 2 is a block diagram of an illustrative COPD severity estimationsystem, in accordance with various embodiments.

FIGS. 3-6 are block diagrams of various arrangements of variouscomponents of the control logic of the COPD severity estimation systemof FIG. 2, in accordance with various embodiments.

FIG. 7 is a perspective view of an example arrangement portion of aportion of the COPD severity estimation system of FIG. 2, in accordancewith various embodiments.

FIGS. 8-10 are block diagrams of various arrangements of variouscomponents of the control logic of the COPD severity estimation systemof FIG. 2, in accordance with various embodiments.

FIG. 11 is a flow diagram of an illustrative process for COPD severityestimation, in accordance with various embodiments.

FIG. 12 is an example recurrence plot of breathing and locomotion datafor a COPD patient, along with the associated time series, that may begenerated by the COPD severity estimation system of FIG. 2, inaccordance with various embodiments.

FIG. 13 is a representation of an illustrative data structure forstoring data generated and/or used by the COPD severity estimationsystem of FIG. 2, in accordance with various embodiments.

FIG. 14 is a representation of an illustrative display for displayingdata generated and/or used by the COPD severity estimation system ofFIG. 2, in accordance with various embodiments.

FIG. 15 includes graphs from the COPD severity estimation studyillustrating representative segment selection for two healthy youngadults walking at a self-selected pace with no breathing instructions.

FIG. 16 includes graphs from the COPD severity estimation studyillustrating filtered breathing and locomotion data from arepresentative healthy young adult for one minute of walking at aself-selected pace with no breathing instructions.

FIGS. 17-19 are recurrence plots of breathing and locomotion data forvarious subjects, along with the associated time series, in the COPDseverity estimation study.

FIG. 20 illustrates the interaction of entropy between the breathing andlocomotion time series in patients with COPD compared with theage-matched controls in the COPD severity estimation study.

FIG. 21 is a block diagram of an example computing device that may besuitable for use in practicing various ones of the disclosedembodiments.

DETAILED DESCRIPTION

Disclosed herein are embodiments of systems and techniques forestimating the severity of chronic obstructive pulmonary disease (COPD)in a patient. For example, in some embodiments, a system for estimatingCOPD severity in a patient may include logic to receive a breathingsignal representative of breathing activity of the patient over a timeinterval, receive a locomotion signal representative of locomotiveactivity of the patient over the time interval, and provide breathingdata and locomotion data to additional logic, wherein the additionallogic is to generate an estimate of COPD severity in the patient bycomparison of 1) a cross-recurrence quantification analysis (cRQA)parameter between the breathing data and the locomotion data and 2) areference value. The breathing data may be based on the breathingsignal, and the locomotion data may be based on the locomotion signal.

Conventional techniques for COPD severity classification may not providea resolution sufficient to detect small and gradual worsening of apatient's condition. As a result, patients may suffer dangerous andcostly exacerbations of their symptoms without warning. The magnitude ofthis problem is growing; COPD is the only chronic disease currently onthe rise in the United States. The systems and techniques disclosedherein may provide a more accurate and timely estimate of COPD severitythat may enable patients and care providers to track the progress of thedisease more closely (e.g., as the disease worsens).

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof wherein like numeralsdesignate like parts throughout, and in which is shown, by way ofillustration, embodiments that may be practiced. It is to be understoodthat other embodiments may be utilized and structural or logical changesmay be made without departing from the scope of the present disclosure.Therefore, the following detailed description is not to be taken in alimiting sense.

Various operations may be described as multiple discrete actions oroperations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description should notbe construed as to imply that these operations are necessarily orderdependent. In particular, these operations may not be performed in theorder of presentation. Operations described may be performed in adifferent order from the described embodiment. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B and C).

The description uses the phrases “in an embodiment” or “in embodiments,”which may each refer to one or more of the same or differentembodiments. Furthermore, the terms “comprising,” “including,” “having,”and the like, as used with respect to embodiments of the presentdisclosure, are synonymous. As used herein, the term “logic” may referto an Application Specific Integrated Circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and/or memory(shared, dedicated, or group) that execute one or more software orfirmware programs, a combinational logic circuit, and/or other suitablehardware that provide the described functionality. As used herein, theterm “housing” may refer to an enclosure (partial or full) for logic(e.g., a plastic or metal “box” used to at least partially containcircuitry) or another physical support for logic (e.g., a rack or acircuit board substrate).

FIG. 1 depicts an illustrative computing system 100 configured for COPDseverity estimation, in accordance with various embodiments. In someembodiments, the computing system 100 may be configured to receive abreathing signal representative of breathing activity of the patientover a time interval, receive a locomotion signal representative oflocomotive activity of the patient over the time interval, and providebreathing data and locomotion data to additional logic. The additionallogic (which may be included in the computing system 100 or may beseparate from the computing system 100, as discussed below) may beconfigured to generate an estimate of COPD severity in the patient bycomparison of 1) a cRQA parameter between the breathing data and thelocomotion data and 2) a reference value. The breathing data may bebased on the breathing signal, and the locomotion data may be based onthe locomotion signal.

The computing system 100 may include a wearable computing device 102, apatient personal computing device 104, a dock computing device 106, aremote computing device 108, and a care provider computing device 110.Each of the wearable computing device 102, the patient personalcomputing device 104, the dock computing device 106, the remotecomputing device 108, and the care provider computing device 110 mayinclude COPD estimation components (illustrated in FIG. 1 as COPDestimation components 112, 114, 116, 118, and 120, respectively). COPDestimation operations may be distributed between the COPD estimationcomponents 112, 114, 116, 118, and 120 of the computing system 100 assuitable. Several examples of the distribution of operations between thecomponents of the computing system 100 are discussed herein, but anyother combination of more or fewer components and distribution of theoperations may be used. In some embodiments, the computing system 100may be configured as a COPD severity estimation system 200, discussedbelow with reference to FIG. 2. One or more of the computing devices ofthe computing system 100 may be implemented in accordance with theembodiments discussed below with reference to the computing device 2100of FIG. 21.

Communication within the computing system 100 may be enabled by thecommunication pathways illustrated in FIG. 1. The communication pathwaysmay each include wired communication pathways and/or wirelesscommunication pathways, over direct couplings, and/or over personal,local, and/or wide area networks. Each of the wearable computing device102, the patient personal computing device 104, the dock computingdevice 106, the remote computing device 108, and the care providercomputing device 110 may include suitable hardware for supporting thecommunication pathways, such as network interface cards, modems, Wi-Fidevices, Bluetooth devices, and so forth. In some embodiments, thecommunication pathways may be direct communication pathways between thecomponents as illustrated in FIG. 1. As used herein, references to“direct” communication pathways between two components of the computingsystem 100 of FIG. 1 (or any system or device disclosed herein) mayrefer to a communication pathway that does not route through anotherillustrated component but that may route through other non-illustrateddevices (e.g., routers and/or switches). Not all of the communicationpathways illustrated in FIG. 1 may be present in every embodiment of thecomputing system 100. For example, in some embodiments, the wearablecomputing device 102 and/or the patient personal computing device 104may not communicate directly with the care provider computing device 110but may instead do so through an intermediate device (e.g., the dockcomputing device 106 and/or the remote computing device 108).

Each of the computing devices included in the computing system 100 mayinclude a processing device and a storage device (not shown). Theprocessing device may include one or more processing devices, such asone or more processing cores, ASICs, electronic circuits, processors(shared, dedicated, or group), combinational logic circuits, and/orother suitable components that may be configured to process electronicdata. The storage device may include any suitable memory or mass storagedevices (such as solid-state drive, diskette, hard drive, compact discread only memory (CD-ROM), and so forth). Each of the computing devicesincluded in the computing system 100 may include one or more buses (andbus bridges, if suitable) to communicatively couple the processingdevice, the storage device, and any other devices included in therespective computing devices. The storage device may include a set ofcomputational logic, which may include one or more copies of computerreadable media having instructions stored therein, which, when executedby the processing device of the computing device, may cause thecomputing device to implement any of the techniques and methodsdisclosed herein, or any portion thereof. The wearable computing device102, the patient personal computing device 104, the dock computingdevice 106, the remote computing device 108, and the care providercomputing device 110 may each include peripheral devices, which maycommunicate via wired or wireless communication pathways, such ascameras, printers, scanners, radio frequency identification (RFID)readers, credit card swipe devices, or any other peripheral devices.Except for the COPD severity estimation teachings of the presentdisclosure incorporated therein, the wearable computing device 102, thepatient personal computing device 104, the dock computing device 106,the remote computing device 108, and the care provider computing device110 may be a broad range of such devices known in the art. Specific, butnot limiting, examples are described below. In some embodiments, thecomputational logic may include any of the logic discussed below withreference to FIG. 2.

The wearable computing device 102 may be a computing device that isintegrated into a garment, accessory, or other support structure that isconfigured to be worn on the body of the user (or “wearer”). Examples ofsuitable support structures for the wearable computing device 102 mayinclude glasses, a headset, a hair accessory (e.g., headband orbarrette), an ear piece, jewelry (e.g., brooch, earrings, or necklace),a wristband (e.g., wristwatch), a neckband (e.g., tie or scarf), agarment (e.g., shirt, pants, dress skirt, or jacket), a hat, shoes, alanyard or nametag, or an implantable support structure, among others.In some embodiments, the wearable computing device 102 may include(e.g., as a support structure) a wearable sensor for measuringphysiological signals or other signals representative of the wearer'sbehavior. For example, as discussed below with reference to FIG. 7, awearable computing device 102 may include a chest strap sensor thatwraps around a wearer's torso and measures the expansion and contractionof the wearer's torso during breathing. In some embodiments, thewearable computing device 102 may include a wrist-mounted computingdevice. A wrist-mounted computing device may itself include a sensor(e.g., for measuring pulse rate and/or locomotion) and/or may be inwired or wireless communication with a sensor (e.g., a chest strapsensor). In some embodiments, the wearable computing device 102 may be aglasses-mounted computing device and may take the form of any of theembodiments discussed above with reference to the wrist-mountedcomputing device. COPD severity estimation and other operationsperformed by the wearable computing device 102 may be controlled by anapp or plug-in on the wearable computing device 102, for example.

The patient personal computing device 104 may be a computing deviceaccessible by a patient and configured for carrying in a pocket, abackpack, or other carrying case, or configured to rest semipermanentlyon a surface (e.g., as a server does in a rack or a desktop computerdoes on a desk). Examples of personal computing devices that may serveas the patient personal computing device 104 include cellular phones,smartphones, other personal mobile communication devices, tablets,electronic book readers, personal digital assistants, laptops, desktops,or other such computing devices. COPD severity estimation and otheroperations performed by the patient personal computing device 104 may becontrolled by an app or plug-in on the patient personal computing device104, for example. In some embodiments, the patient personal computingdevice 104 may have more computing resources (e.g., processing power,memory, and/or communication bandwidth) than the wearable computingdevice 102. Thus, in some embodiments, data captured and preliminarilyprocessed by the wearable computing device 102 (e.g., sensor datarepresentative of a patient's physiology or behavior) may be transmittedover a communication pathway to the patient personal computing device104 for further processing.

The dock computing device 106 may be a computing device accessible by apatient and configured with one or more connectors to receive matingconnectors of another computing device (e.g., the wearable computingdevice 102 or the patient personal computing device 104). In someembodiments, when another computing device is mated with the dockcomputing device 106, data may be transferred from the other computingdevice to the dock computing device 106, from the dock computing device106 to the other computing device, or both. In some embodiments, whenanother computing device is mated with the dock computing device 106,the dock computing device 106 may provide power to the other computingdevice to charge one or more batteries or other energy storage devicesof the other computing device. In some embodiments, the dock computingdevice 106 may have more computing resources (e.g., processing power,memory, and/or communication bandwidth), then the other computing device(e.g., a wearable computing device 102 or a patient personal computingdevice 104). Thus, in some embodiments, data captured and preliminarilyprocessed by the other computing device may be transmitted over acommunication pathway to the dock computing device 106 for furtherprocessing. In some embodiments, the dock computing device 106 mayinclude a Raspberry Pi single-board computing device for performinginter-computing device communication and data transfer and storage.

In some embodiments, the dock computing device 106 may be in wired orwireless communication with the patient personal computing device 104(e.g., a laptop or tablet), and a wearable computing device 102 may bemated with the dock computing device 106. In such an embodiment, datamay be transferred from the wearable computing device 102 to the dockcompeting device 106, and then from the dock computing device 106 to thepatient personal computing device 104. The data may remain on thepatient personal computing device 104, or may be transferred by thepatient personal computing device 104 to another computing device (e.g.,the remote computing device 108, discussed below). Thus, in some suchembodiments, the dock computing device 106 may act as an intermediarybetween a wearable computing device 102 and a patient personal computingdevice 104. Further examples of such arrangements are discussed indetail below.

In some embodiments, the dock computing device 106 may not be in wiredor wireless communication with a patient personal computing device 104,but may include its own communication device for communication betweenthe dock computing device 106 of the remote computing device (e.g., theremote computing device 108). An example communication device may be awireless transceiver (e.g., for wireless cellular communications). Insome such embodiments, when a wearable computing device 102 is matedwith the dock computing device 106, data may be transferred from thewearable computing device 102 to the dock computing device 106, and thedock computing device 106 may use its own communication device totransfer the data to a remote computing device. Further examples of sucharrangements are discussed in detail below.

The remote computing device 108 may include one or more servers (e.g.,arranged in a “cloud” computing configuration) or other computingdevices remote from the wearable computing device 102, the patientpersonal computing device 104, and the dock computing device 106. Thecommunication pathway between the wearable computing device 102 and theremote computing device 108, the communication pathway between thepatient personal computing device 104 and the remote computing device108, and the communication pathway between the dock computing device 106on the remote computing device 108 may be configured according to anyremote wired or wireless communication protocol. In some embodiments,the remote computing device 108 may have more computing resources (e.g.,processing power, memory, and/or communication bandwidth) than thewearable computing device 102, the patient personal computing device104, or the dock computing device 106. Thus, in some embodiments, datacaptured and preliminarily processed by the wearable computing device102, the patient personal computing device 104, and/or the dockcomputing device 106 (e.g., sensor data representative of a patient'sphysiology or behavior) may be transmitted over the appropriatecommunication pathways to the remote computing device 108 for furtherprocessing. In some embodiments, the remote computing device 108 mayperform most of the COPD severity estimation conversational operationsdiscussed below with reference to FIG. 2, including those performed bythe estimate generation logic 228.

The care provider computing device 110 may be a computing deviceaccessible by a care provider (e.g., doctor, nurse, care facility, orother entity that monitors a patient's health) and configured forcarrying in a pocket, backpack, or other carrying case or configured torest semipermanently on a surface (e.g., as discussed above withreference to the patient personal computing device 104). COPD severityestimation and other operations performed by the care provider personalcomputing device 110 may be controlled by an app or plug-in on the careprovider computing device 110, for example. In some embodiments, thecare provider computing device 110 may be in direct communication withthe wearable computing device 102, the patient personal computing device104, and/or the dock computing device 106, while in other embodiments,the care provider computing device 110 may be in indirect communicationwith one or more of the wearable computing device 102, the patientpersonal computing device 104, and/or the dock computing device 106 viathe remote computing device 108.

In some embodiments, the remote computing device 108 may communicatewith multiple personal computing devices (configured similarly to thepatient personal computing device 104) corresponding to multipledifferent patients and/or multiple wearable computing devices(configured similarly to the wearable computing device 102)corresponding to multiple different patients. The remote computingdevice 108 may perform similar processing and storage operations foreach personal or wearable computing device (and thus for each of thedifferent patients). In some embodiments, a single care providercomputing device 110 may be in communication with the remote computingdevice 108 and may access the data for the multiple patients. The remotecomputing device 108 may devote different resources to different ones ofthe plurality of personal or wearable computing devices in communicationwith the remote computing device 108 (e.g., different memory partitionsor databases for each device).

Although a single wearable computing device 102, a single patientpersonal computing device 104, a single dock computing device 106, asingle remote computing device 108, and a single care provider computingdevice 110 are illustrated in FIG. 1, this is simply for convenience,and the computing system 100 may include multiple ones of any of thecomputing devices. For example, in some embodiments, a patient may weara first wearable computing device 102 on her wrist (e.g., to measureheart rate) and a second wearable computing device 102 on her chest(e.g., coupled with a chest strap sensor to measure breathing); thesewearable computing devices 102 may be in communication with each otherand/or with a patient personal computing device 104 or a dock computingdevice 106 as discussed above. A single care provider may have access tomultiple care provider computing devices 110 (e.g., a desktop computerin her office and a portable tablet). The remote computing device 108may include a first remote computing device 108 for nonvolatile storageof COPD severity estimation data and a second remote computing device108 for processing of COPD severity estimation data. The aboveembodiments are simply examples, and any suitable number of varioustypes of computing devices may be included in the computing system 100.

In some embodiments of the COPD severity estimation systems disclosedherein, one or more of the components of the computing system 100 shownin FIG. 1 may not be included. For example, in some embodiments, thecomputing system 100 may not include a wearable computing device 102. Insome embodiments, the computing system 100 may not include a patientpersonal computing device 104. In some embodiments, the computing system100 may not include a dock computing device 106. In some embodiments,the computing system 100 may not include a remote computing device 108.In some embodiments, the computing system 100 may not include a careprovider computing device 110. In some embodiments, one or more of thecommunication pathways illustrated in FIG. 1 between components of thecomputing system 100 may not be included, as noted above.

FIG. 2 depicts an illustrative COPD severity estimation system 200. Asdiscussed above with reference to the computing system 100, the COPDseverity estimation system 200 may be configured to perform any of anumber of COPD severity estimation operations. For example, the COPDseverity estimation system 200 may be configured to receive a breathingsignal representative of breathing activity of the patient over a timeinterval, receive a locomotion signal representative of locomotiveactivity of the patient over the time interval, and provide breathingdata and locomotion data for generation of an estimate of COPD severityin the patient by comparison of 1) a cRQA parameter between thebreathing data and the locomotion data and 2) a reference value. Thebreathing data may be based on the breathing signal, and the locomotiondata may be based on the locomotion signal.

The COPD severity estimation system 200 may be implemented by thecomputing system 100 of FIG. 1, in accordance with various embodiments.In particular, the components of the COP severity estimation system 200may be distributed in any suitable manner among one or more of the COPDestimation components 112, 114, 116, 118, and 120 of the computingsystem 100. Although a number of components are illustrated in FIG. 2,various embodiments may omit components as appropriate for the COPDseverity estimation operations to be performed. For example, someembodiments of the COPD severity estimation system 200 may not beconfigured for heart rate data processing, and thus may not include theheart rate sensor 210 or the heart rate logic 232. In another example,some embodiments of the COPD severity estimation system 200 may not beconfigured for patient locomotion prompting, and thus may not include alocomotion prompt logic 234. In some embodiments, the components of theCOPD severity estimation system 200 may be implemented by one or morecomputing devices, such as a computing device 2100 of FIG. 21 (discussedbelow).

The COPD severity estimation system 200 may include one or moreinput/output (I/O) devices 202. The one or more I/O devices 202 mayinclude a communication device 208, a breathing sensor 212, a locomotionsensor 214, a heart rate sensor 210, other sensors 216, a display device218, and/or other I/O devices 220. Although various ones of the I/Odevices 202 (and other components described herein) may be referred toin the singular, any number of I/O devices 202 of any type may beincluded in the I/O devices 202 (and similarly, any component mayinclude multiple such components). As noted above, various ones of theI/O devices 202 included in an embodiment of the COPD severityestimation system 200 may be distributed as suitable between computingdevices of the computing system 100. For example, in some embodiments, abreathing sensor 212 may be included in a wearable computing device 102,while a display device 218 may be included in the care providercomputing device 110. In another example, wired communication devices ofthe communication device 208 may be included in the wearable computingdevice 102 and the dock computing device 106 for wired communicationtherebetween, and the dock computing device 106 and the remote computingdevice 108 may include wireless communication devices 208 forcommunication therebetween.

The I/O devices 202 may include a breathing sensor 212. In someembodiments, the breathing sensor may include a chest strap sensorconfigured to be worn around the patient's chest. The breathing sensormay be a resistive sensor and formed of a conductive material; as thepatient breathes, the expansion and contraction of the patient's chestmay stretch and then release the conductive material, changing theresistance between contact points located at different locations aroundthe circumference of the patient's chest. The changing resistance of theconductive material may be monitored by a breathing signal receipt logic222, and may be used as the breathing signal. Alternatively, a constantcurrent or voltage may be provided to the resistive chest strap sensor,and changes in voltage across or current through the sensor,respectively, may be used as the breathing signal. In some embodiments,a chest strap sensor may be a capacitive sensor and may have portionsthat act as capacitive plates separated by the patient's chest (whichacts as a dielectric the value of which changes as the patientbreathes). The breathing signal receipt logic 222 may apply a fixedfrequency voltage signal to the capacitive plates, and the distortion ofthat fixed frequency voltage signal (measured as, e.g., changes in dutycycle), may provide the breathing signal.

In some embodiments, the breathing sensor 212 may measure the flow ofair out of a patient's nose and/or mouth. For example, the breathingsensor 212 may include a nasal cannula. 214 may include one or moresensors able to detect locomotive activity of a patient (e.g., walking,running, or other locomotive activity). The locomotion sensor 214 may belocated in any suitable computing device or computing devices of thecomputing system 100 (e.g., in the wearable computing device 102). Insome embodiments, the locomotion sensor 214 may include a pressuresensor configured for use inside a patient's shoe or sock; as thepatient takes steps, the changing pressure on the sensor may be used asthe locomotion signal.

In some embodiments, the locomotion sensor 214 may include one or moreaccelerometers. As is well known, acceleration data generated by anaccelerometer may be integrated once to provide velocity data, and twiceto provide location data. Any of these types of data may be used aslocomotion data, as suitable. In some embodiments, the locomotion datamay include a step count, an altitude change, or any other suitablequantification of the patient's movement. An accelerometer may be aone-, two-, or three-axis accelerometer. In some embodiments, thelocomotion sensor 214 may be an inertial measurement unit (IMU) thatincludes a gyroscope and a three-axis accelerometer. In suchembodiments, some or all of the data generated by the IMU may beutilized to provide the locomotion sensor 214. For example, in someembodiments, the gyroscope data may be ignored, and data from one ormore axis of the three-axis accelerometer may be used to generate thelocomotion data. One or more accelerometers may be located at anysuitable location or locations on the patient's body. For example, oneor more accelerometers may be mounted in an eyeglasses-type support, ina bracelet-type support designed to be worn on the patient's ankle, in abracelet-type support designed to be worn on the patient's wrist, in agarter-type support designed to be worn on the patient's thigh, in aband designed to be worn around the patient's waist or chest, or in ahousing designed to clip to a patient's belt, pants, or skirt. These aresimply illustrative examples, and any suitable support may be used tomount one or more accelerometers. In some embodiments, one accelerometermay be positioned at one location on a patient's body, while anotheraccelerometer may be positioned at another location on the user's body(e.g., on the wrist and on the torso).

In some embodiments, the locomotion sensor 214 may include one or moreimage capture devices, such as one or more digital cameras included inthe wearable computing device 102. In such embodiments, the locomotionof the patient may be determined by analysis of the images captured bythe image capture device as the patient moves. This analysis may includeconventional video processing techniques for determining speed ofmotion, direction of motion, and type of motion, for example. One ormore image capture devices may be located in a suitable location orlocations on the patient's body. For example, one or more image capturedevices may be mounted in an eyeglasses-type support and may be“outward” facing to image the environment around the patient as thepatient moves.

In some embodiments, the locomotion sensor 214 may be configured tostream locomotion data (e.g., accelerometer or video data) to otherdevices via a wired or wireless communication pathway. For example, thelocomotion sensor 214 may be included in the wearable computing device102, and may stream locomotion data wirelessly to the patient personalcomputing device 104. In other embodiments, the locomotion sensor 214may be included in the wearable computing device 102, and the wearablecomputing device 102 may store the locomotion data generated by thelocomotion sensor 214 in a storage device local to the wearablecomputing device 102 (e.g., a storage device 206). In some suchembodiments, the wearable computing device 102 may provide thelocomotion data to the dock computing device 106 upon mating thewearable computing device 102 with the dock computing device 106, or thewearable computing device 102 may provide the locomotion data to thepatient personal computing device 104 via a wired or wirelesscommunication pathway. In other embodiments, the wearable computingdevice 102 may provide the locomotion data directly to the remotecomputing device 108 (e.g., via a wireless communication pathway).

In some embodiments, the I/O devices 202 may include a heart rate sensor210. The heart rate sensor 210 may be any commercially available heartrate sensor 210, such as a chest strap sensor and associated processingelectronics. In some embodiments, the breathing sensor 212 and the heartrate sensor 210 may share components. For example, the breathing sensor212 and the heart rate sensor may both utilize a chest strap substrate;the breathing sensor 212 may generate a breathing signal from the cheststrap substrate by monitoring changes in resistance or capacitance, asdiscussed above, while the heart rate sensor 210 may generate heart ratedata from the chest strap substrate by monitoring an electrocardiogram(EKG) sensed by electrodes mounted on or included in the chest strapsubstrate. In some embodiments, the heart rate sensor 210 may generateheart rate data by monitoring another part of the body, such as thewrist. The heart rate data generated by the heart rate sensor 210 may bea raw or processed EKG, or may be a signal expressed in beats per timeunit.

In some embodiments, the I/O devices 202 may include one or more othersensors 216. In some embodiments, the other sensors 216 may include anybuttons, switches, dials, or other interface devices configured to beactuated by a user (e.g., the patient or a care provider). For example,the other sensors 216 may include buttons that are pressable by a userto start receipt of a breathing or locomotion signal, reset receipt of abreathing or locomotion signal (e.g., by wiping breathing or locomotioninformation stored in the storage device 206), end receipt of abreathing or locomotion signal, or control any other operation of theCOPD severity estimation system 200. In some embodiments, the othersensors 216 may include an altimeter, a humidity sensor, an ambientpressure sensor, and/or a temperature sensor (e.g., amicroelectromechanical systems (MEMS) sensor that may measuretemperature and pressure). The data from these sensors may be used toinform the level of a patient's effort during locomotion.

In some embodiments, the I/O devices 202 may include one or morecommunication devices 208. The communication device 208 may enable wiredand/or wireless communications for the transfer of data to, from, and/orbetween components of the COPD severity estimation system 200 (e.g., to,from, and/or between components of the computing system 100). Thecommunication device 208 may include multiple communication devices,with one or more communication devices included in each component of thecomputing system 100. For example, the communication device 208 maysupport one or more wired communication protocols, such as I2C,universal serial bus (USB), SPI, or any other communication protocol.

The term “wireless” and its derivatives may be used to describecircuits, devices, systems, methods, techniques, communicationschannels, etc., that may communicate data through the use of modulatedelectromagnetic radiation through a nonsolid medium. The term does notimply that the associated devices do not contain any wires, although insome embodiments they might not. The communication device 208 mayimplement any of a number of wireless standards or protocols, includingbut not limited to Institute for Electrical and Electronic Engineers(IEEE) standards including Wi-Fi (IEEE 802.11 family), IEEE 802.16standards (e.g., IEEE 802.16-2005 Amendment), Long-Term Evolution (LTE)project along with any amendments, updates, and/or revisions (e.g.,advanced LTE project, ultra mobile broadband (UMB) project (alsoreferred to as “3GPP2”), etc.). IEEE 802.16-compatible BroadbandWireless Access (BWA) networks are generally referred to as WiMAXnetworks, an acronym that stands for Worldwide Interoperability forMicrowave Access, which is a certification mark for products that passconformity and interoperability tests for the IEEE 802.16 standards. Thecommunication device 208 may operate in accordance with a Global Systemfor Mobile Communication (GSM), General Packet Radio Service (GPRS),Universal Mobile Telecommunications System (UMTS), High Speed PacketAccess (HSPA), Evolved HSPA (E-HSPA), or LTE network. The communicationdevice 208 may operate in accordance with Enhanced Data for GSMEvolution (EDGE), GSM EDGE Radio Access Network (GERAN), UniversalTerrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN).The communication device 208 may operate in accordance with CodeDivision Multiple Access (CDMA), Time Division Multiple Access (TDMA),Digital Enhanced Cordless Telecommunications (DECT), Evolution-DataOptimized (EV-DO), and derivatives thereof, as well as any otherwireless protocols that are designated as 3G, 4G, 5G, and beyond. Thecommunication device 208 may operate in accordance with other wirelessprotocols in other embodiments.

Multiple communication devices included in the communication device 208may enable communication in accordance with different communicationprotocols. For instance, a first communication chip may be dedicated toshorter-range wireless communications such as Wi-Fi and Bluetooth, and asecond communication chip may be dedicated to longer range wirelesscommunications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, andothers. In another example, a first communication chip may be dedicatedto wired communications, and a second communication chip may bededicated to wireless communications.

In some embodiments, the I/O devices 202 may include a display device218. The display device 218 may provide a visual representation of datacaptured by the COPD severity estimation system 200 and/or datagenerated by the COPD severity estimation system 200 (e.g., COPDseverity estimates). The display device 218 may include one or moreheads-up displays (i.e., displays including a projector arranged in anoptical collimator configuration and a combiner to provide data withoutrequiring a user to look away from his or her typical viewpoint),computer monitors, projectors, touchscreen displays, liquid crystaldisplays (LCDs), light-emitting diode displays or flat panel displays,for example.

In some embodiments, the I/O devices 202 may include other I/O devices220. Examples of other I/O devices 220 may include a keyboard, a cursorcontrol device such as a mouse, a stylus, a touchpad, a bar code reader,a Quick Response (QR) code reader, an RFID reader, a GPS receiver, anaudio capture device (which may include one or more microphones arrangedin various configurations), one or more speakers or other audiotransducers (which may be, e.g., mounted in one or more earphones orearbuds), printers, projectors, or any suitable I/O device.

The COPD severity estimation system 200 may include a control logic 204.The control logic 204 may include an I/O device interface 238 configuredto receive data from and/or provide data to the I/O devices 202, andlogic components configured to control the operation of the COPDseverity estimation system 200. The I/O device interface 238 may includehardware to support any suitable communications between the I/O devices202 and the control logic 204. Examples of communications protocols thatmay be supported by the I/O device interface 238 may include any of thewired and/or wireless communication protocols discussed above withreference to the communication device 208. In some embodiments, the I/Odevice interface 238 may support the inter-integrated circuit (I2C)protocol and/or the serial peripheral interface (SPI) protocol. Forexample, various ones of the sensors of the I/O devices 202 maycommunicate with a processor of the control logic 204 via the I2Cprotocol (e.g., an altimeter, a humidity sensor, a temperature sensor,and the locomotion sensor 214) and other ones of the sensors maycommunicate with a processor of the control logic 204 via the SPIprotocol (e.g., the breathing sensor 212 and a USB connector of theother I/O devices 220). The I/O device interface 238 may also includehardware to support communication with the storage device 206. Forexample, communication between the control logic 204 and a micro securedigital (SD) card of the storage device 206 may be conducted via SPIprotocol.

Although the components of the control logic 204 are illustratedseparately, the components may be combined or divided as suitable, andeach may use one or more of the results generated by others inperforming its own analysis. Data may be communicated between thecomponents of the control logic 204 over a physical bus, a long-distancewired communication pathway, a short- or long-distance wirelesscommunication pathway, or any combination of communication pathways(e.g., any of the communication pathways or protocols discussed abovewith reference to the communication device 208 and the I/O deviceinterface 238).

The COPD severity estimation system 200 may include a storage device 206(which may, as discussed above, include multiple storage devices). Insome embodiments, the storage device 206 may include a database or otherdata storage structure which may include memory structures for storingany of the data described herein used for COPD severity estimationoperations (e.g., as discussed below with reference to FIG. 13). Thestorage device 206 may include any volatile or nonvolatile memorydevice, such as one or more hard drives, solid-state logic, or portablestorage media, for example.

The control logic 204 may include breathing signal receipt logic 222,which may be configured to receive a breathing signal representative ofbreathing activity of a patient. The breathing signal receipt logic 222may be coupled with the I/O devices 202, and may receive the breathingsignal from the breathing sensor 212 (e.g., via the I/O device interface238). In some embodiments, the breathing signal receipt logic 222 may beimplemented by a programmed microcontroller that may be coupled with abreathing sensor 212. This coupling may be permanent or selectable inthat the breathing signal receipt logic 222 and the breathing sensor 212can be readily coupled and uncoupled. In some embodiments, the breathingsignal receipt logic 222 may receive the breathing signal by storing thebreathing signal (and/or a processed version of the breathing signal) inthe storage device 206.

The breathing signal may take any suitable form to represent breathingactivity of the patient. For example, in some embodiments, the breathingsignal may represent contraction/expansion of the patient's chest duringbreathing (e.g., as measured by a resistive or capacitive chest strapsensor that at least partially encircles the patient's chest, or asmeasured by another motion sensor responsive to the motion of apatient's chest during breathing). In some embodiments, the breathingsignal may represent the volume and/or rate of airflow during breathing(e.g., as measured by a nasal cannula). In some embodiments, thebreathing signal may be expressed in breaths per time unit.

The breathing signal receipt logic 222 may receive a breathing signalover a time interval. The duration of that time interval may be dictatedby the amount of data storage to which the breathing signal receiptlogic 222 has access in the storage device 206; once receipt of abreathing signal commences, it may continue until there is no availabledata storage, and may be recommenced once storage is available. Theduration of that time interval may be predetermined to a fixed amount,and the breathing signal receipt logic 222 may include a timer forceasing receipt (or storage) of the breathing signal after the fixedamount has elapsed. The duration of breathing signal received should beadequate to capture the breathing phenomena of interest. In someembodiments, at least 45 seconds of breathing data may be received bythe breathing signal receipt logic 222. The breathing signal receiptlogic 222 may normalize a received breathing signal such that themaximum magnitude of the breathing signal over the time interval has avalue of 1.

Receipt of the breathing signal by the breathing signal receipt logic222 (e.g., storage of the breathing signal in the storage device 206)may be initiated in any suitable manner. For example, in someembodiments, the breathing signal receipt logic 222 may initiate receiptof the breathing signal in response to detection of breathing activityof the patient (e.g., by detecting activity from the breathing sensor212 above a predetermined threshold that distinguishes likely noise frombreathing activity). In some embodiments, the breathing signal receiptlogic 222 may initiate receipt of the breathing signal in response tothe patient or a care provider pressing an “on” or “start” button. Insome embodiments, the breathing signal receipt logic 222 may initiatereceipt of the breathing signal at a predetermined time (e.g., 9:00 AMevery day). In some embodiments, the breathing sensor 212 may include achest strap sensor, and the breathing signal receipt logic 222 may beconfigured to receive the breathing signal from the chest strap sensor.In some embodiments, the breathing signal receipt logic 222 may beconfigured with an analog or digital filter to filter the breathingsignal upon receipt (e.g., to generate locomotion data). For example,the breathing signal receipt logic 222 may low-pass filter the breathingsignal with an appropriate cutoff frequency to reduce noise. A cutofffrequency of 0.5 Hertz may be suitable. In other embodiments, nofiltering or different filtering may be performed.

The control logic 204 may include a locomotion signal receipt logic 224,which may be configured to receive a locomotion signal representative oflocomotive activity of the patient. The locomotion signal receipt logic224 may be coupled with the I/O devices 202, and may receive thelocomotion signal from the locomotion sensor 214 (e.g., via the I/Odevice interface 238). In some embodiments, the locomotion signalreceipt logic 224 may be implemented by a programmed microcontrollerthat may be coupled with a locomotion sensor 214. This coupling may bepermanent or selectable in that the locomotion signal receipt logic 224and the locomotion sensor 214 can be readily coupled and uncoupled. Insome embodiments, the locomotion signal receipt logic 224 may receivethe locomotion signal by storing the locomotion signal (and/or aprocessed version of the locomotion signal) in the storage device 206.

The locomotion signal may take any suitable form to represent locomotiveactivity of the patient. For example, the locomotion signal mayrepresent the up-and-down and/or forward-and-backward motion of one ormore body parts of the patient during locomotion (e.g., as measured byan accelerometer). In some embodiments, the locomotion signal mayrepresent a pressure experienced by the patient's foot against a shoe orthe ground during walking (e.g., as measured by a pressure sensor in thepatient's shoe or sock). In some embodiments, the locomotion signal maybe expressed in steps per time unit.

The locomotion signal receipt logic 224 may receive a locomotion signalover a time interval. The duration of that time interval may be dictatedby the amount of data storage to which the locomotion signal receiptlogic 224 has access in the storage device 206; once receipt of alocomotion signal commences, it may continue until there is no availabledata storage, and may be recommenced once storage is available. Theduration of that time interval may be predetermined to a fixed amount,and the locomotion signal receipt logic 224 may include a timer forceasing receipt (or storage) of the locomotion signal after the fixedamount has elapsed. The duration of locomotion signal received should beadequate to capture the locomotion phenomena of interest. In someembodiments, at least 45 seconds of locomotion data may be received bythe locomotion signal receipt logic 224. The locomotion signal receiptlogic 224 may normalize a received locomotion signal such that themaximum magnitude of the locomotion signal over the time interval has avalue of 1 (as measured, e.g., in multiples of the acceleration due togravity).

In some embodiments, the locomotion signal receipt logic 224 may beconfigured with an analog or digital filter to filter the locomotionsignal upon receipt (e.g., to generate locomotion data). For example,the locomotion signal receipt logic 224 may low-pass filter thelocomotion signal with an appropriate cutoff frequency to reduce noise.A cutoff frequency of 10 Hertz may be suitable. In other embodiments, nofiltering or different filtering may be performed.

Receipt of the locomotion signal by the locomotion signal receipt logic224 (e.g., storage of the locomotion signal in the storage device 206)may be initiated in any suitable manner. For example, in someembodiments, the locomotion signal receipt logic 224 may initiatereceipt of the locomotion signal in response to detection of locomotiveactivity of the patient (e.g., by detecting activity from the locomotionsensor 214 above a predetermined threshold that distinguishes likelynoise from locomotive activity). In some embodiments, the locomotionsignal receipt logic 224 may initiate receipt of the locomotion signalin response to the patient or a care provider pressing an “on” or“start” button. In some embodiments, the locomotion signal receipt logic224 may initiate receipt of the locomotion signal at a predeterminedtime.

The control logic 204 may include a data provision logic 226, which maybe configured to provide breathing data and locomotion data to theestimate generation logic 228 (discussed in detail below). The dataprovision logic 226 may be coupled with the breathing signal receiptlogic 222, and the breathing data may be based on the breathing signalreceived by the breathing signal receipt logic 222. The data provisionlogic 226 may be coupled with the locomotion signal receipt logic 224,and the locomotion data may be based on the locomotion signal receivedby the locomotion signal receipt logic 224. In some embodiments, thedata provision logic 226 may normalize the breathing data and/or thelocomotion data so that the maximum magnitude of the data over a timeinterval of interest is 1 to facilitate cRQA.

In some embodiments, the data provision logic 226 may be configured withan analog or digital filter to filter the breathing signal and/or thelocomotion signal to generate the breathing data and/or locomotion data,respectively, before providing the breathing data and/or locomotiondata, respectively, to other logic (e.g., instead of or in addition to afilter applied by the breathing signal receipt logic 222 and/or thelocomotion signal receipt logic 224, respectively). For example, thedata provision logic 226 may low-pass filter the breathing signal withan appropriate cut-off frequency (e.g., 0.5 Hertz). For example, thedata provision logic 226 may low-pass filter the locomotion signal withan appropriate cut-off frequency (e.g., 10 Hertz). In some embodiments,the breathing data provided by the data provision logic 226 may be thebreathing signal (or a portion thereof) received by the breathing signalreceipt logic 222. In some embodiments, the locomotion data provided bythe data provision logic 226 may be the locomotion signal (or a portionthereof) received by the locomotion signal receipt logic 224. The dataprovision logic 226 may access the breathing data (or generate thebreathing data based on access to the breathing signal) from a storagedevice 206 commonly accessible to the data provision logic 226 and thebreathing signal receipt logic 222. The breathing data and/or locomotiondata may be stored as comma delimited text files, in some embodiments.

The data provision logic 226 may access the locomotion data (or generatethe locomotion data based on access to the locomotion signal) from astorage device 206 commonly accessible to the data provision logic 226and the locomotion signal receipt logic 224. For example, the dataprovision logic 226 may interface with a storage device 206 that is alsoaccessible to the breathing signal receipt logic 222 and the locomotionsignal receipt logic 224 (e.g., via a USB connection from the dataprovision logic 226 to the storage device 206). The data provision logic226 may enumerate the storage device 206 as a mass storage device, andupon successful enumeration, the data from the storage device may betransferred to the data provision logic 226 (e.g., via USB). In someembodiments, the data provision logic 226 may compress the breathing andlocomotion data before providing the breathing and locomotion data tothe estimate generation logic 228 (e.g., using a ZIP protocol).

In some embodiments, the data provision logic 226 may provide therespiration data and the locomotion data to the estimate generationlogic 228 by providing the respiration data and the locomotion data forstorage in a storage device (e.g., the storage device 206) that isaccessible to the estimate generation logic 228. This commonlyaccessible storage device may be remote from one or both of the dataprovision logic 226 and the estimate generation logic 228. Examples ofsuch embodiments are discussed below with reference to FIG. 9.

The control logic 204 may include estimate generation logic 228, whichmay be configured to generate an estimate of COPD severity in thepatient by comparison of 1) a cRQA parameter comparing the breathingdata and the locomotion data and 2) a reference value. The estimategeneration logic 228 may be coupled with the data provision logic 226and may receive the respiration data and the locomotion data from thedata provision logic 226. In some embodiments, the estimate generationlogic 228 may be implemented by a high-performance computing device,such as a computing cluster or server-based computing device.

The estimate generation logic 228 may generate a cross-recurrence dataset as part of generation of a cRQA parameter. A cross-recurrence dataset comparing a series of vectors X_(i) to a series of vectors Y_(j) maybe denoted R_(XY)(i,j), and may be computed in accordance with

${R_{XY}\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{X_{i} - Y_{j}}} < ɛ} \\0 & {otherwise}\end{matrix} \right.$

where the radius ∈ is positive and the norm is the Euclidean norm. Othernorms may be used, and a value (i,j) for which R_(XY)(i,j) is positivemay be referred to as a “point.” The vector X_(i) may be an embedding ofa time series x_(i) such that

X _(i) =[x _(i) ,x _(i+τ) ,x _(i+2τ) , . . . ,x _(i+mτ)]

where m is the embedding dimension and τ is the delay associated withthe embedding. Consequently, the vector X_(i) may be an m-dimensionalvector. The vector Y_(j) may be analogously embedded based on a timeseries y_(j). Examples of techniques for determining an appropriateradius, embedding dimension, and delay are discussed below withreference to FIG. 11. The vectors X_(i) may represent the breathingdata, and the vectors Y_(j) may represent the locomotion data in thenotation herein.

In some embodiments, the estimate generation logic 228 may generate arecurrence plot of the cross-recurrence data. FIG. 12 is an examplerecurrence plot 1200 of normalized breathing and locomotion data for aCOPD patient, along with plots of an associated breathing data timeseries 1202 and an associated locomotion data time series 1204, that maybe generated by the estimate generation logic 228 of the COPD severityestimation system 200. In a recurrence plot, a point is positioned atlocation (i,j) when R_(XY)(i,j) is equal to 1, and no point ispositioned at other locations.

In some embodiments, the estimate generation logic 228 may generate thepercent determinism of the cross-recurrence data set as a parametercomparing the breathing data and the locomotion data. “Percentdeterminism” is defined herein as the percentage of points that fall ona diagonal line within the recurrence plot. The estimate generationlogic 228 may identify a diagonal line in the cross-recurrence data setwhen at least N points are diagonally adjacent (e.g., {(i,j), (1+1,j+1),. . . , (i+N−1,j+N−1)}), where N is a predetermined integer. A suitablechoice for N may represent a sufficient duration of data to capture thephenomena of interest; a value of N that is too small may spuriouslycapture noise as coordinated signals. For example, in embodiments wherethe data sampling rate is 60 Hertz, a choice of N=6 may represent 100milliseconds of data. This duration may be appropriate, as it mayaccount for corticospinal latency and as any coupling happening underthis time frame may be spurious and/or represent reflex movement. Theestimate generation logic 228 may generate the percent determinism bycalculating the number of points included in a diagonal line divided bythe total points (and multiplying by 100). In a perfectly periodicsystem, a percent determinism will be 100%, while in a system withoutany periodicity, the percent determinism will be closer to 0%. Apatient's COPD severity may be estimated using percent determinism; inparticular, an increased percent determinism value may indicate anincreased COPD severity.

In some embodiments, the estimate generation logic 228 may generate amaximum diagonal line length of the cross-recurrence data set as aparameter comparing the breathing data and the locomotion data. Adiagonal line in the cross-recurrence data set may be defined asdiscussed above, and the estimate generation logic 228 may compute allof the lengths of the diagonal lines in the cross-recurrence data setand identify the maximum value of these lengths as the maximum diagonalline length.

In some embodiments, the estimate generation logic 228 may generate amean diagonal line length of the cross-recurrence data set as aparameter comparing the breathing data and the locomotion data. Adiagonal line in the cross-recurrence data set may be defined asdiscussed above, and the estimate generation logic 228 may compute allof the lengths of the diagonal lines in the cross-recurrence data setand identify the mean value of these lengths as the mean diagonal linelength. A patient's COPD severity may be estimated using mean diagonalline length; in particular, an increased mean diagonal line length valuemay indicate an increased COPD severity.

In some embodiments, the estimate generation logic 228 may generate apercent recurrence of the cross-recurrence data set as a parametercomparing the breathing data and the locomotion data. The estimategeneration logic 228 may generate the percent recurrence by dividing thenumber of points in the cross-recurrence data set by the total number ofpossible points (and multiplying by 100). The percent recurrence mayanalogously be defined as the percentage of the area of a recurrenceplot of the cross-recurrent data set that is occupied by points. Apatient's COPD severity may be estimated using percent recurrence; inparticular, a decreased percent recurrence value may indicate anincreased COPD severity.

In some embodiments, the estimate generation logic 228 may generate anentropy of diagonal line lengths of the cross-recurrence data set as aparameter comparing the breathing data and the locomotion data. Theestimate generation logic 228 may calculate the entropy of diagonal linelengths in accordance with

$- {\sum\limits_{l}{P_{l}\log_{2}P_{l}}}$

where P_(l) is the probability that a diagonal line (defined asdiscussed above) has a length l. The values P_(l) may be generated fromthe estimate generation logic 228 from a histogram of the diagonal linelengths of the cross-recurrence data set. The entropy of diagonal linelengths may represent the probability that the length of a diagonal linewas repeated, and thereby represent the variety of patterns orcomplexity within the two data sets. A patient's COPD severity may beestimated using entropy; in particular, an increased entropy value mayindicate an increased COPD severity.

In some embodiments, the estimate generation logic 228 may generate anyof a number of other parameters comparing the breathing data and thelocomotion data. In some embodiments, one or more such parameters maynot be cRQA parameters. For example, the estimate generation logic 228may generate a cross-correlation of the breathing and locomotion dataseries. The estimate generation logic 228 may calculate thecross-correlation in accordance with

$r_{x,y} = {\sum\limits_{i = 0}^{N - 1}{x_{i}y_{i}}}$

where N is the number of data points in each data series, x_(i) is thei^(th) data point of the first data series, y_(i) is the i^(th) datapoint of the second data series, and r_(x,y) is the correlation.Cross-correlation values range between −1 and +1. In some embodiments,the estimate generation logic may identify and store the maximum valueof the cross-correlation.

In another example, the estimate generation logic 228 may generate afrequency ratio comparing the breathing data and the locomotion data.The frequency ratio may represent the number of steps per breath. Inembodiments where the locomotion data represent the motion of thepatient's heel, steps may be identified by locating heel strikes in thelocomotion data (e.g., at local maxima of a heel acceleration signal).In some embodiments, the estimate generation logic 228 may calculate adiscrete relative phase between the breathing data and the timing ofheel strikes or other indication of steps, as known in the art. Based onthe discrete relative phase value, the number of steps within a breathcan be calculated. For instance, if the discrete relative phase isbetween 0° and 360°, there was one heel strike. However, if the returnedvalue is between 360° and 720°, there were two heel strikes, and soforth.

As noted above, the estimate generation logic 228 may be configured togenerate an estimate of COPD severity in the patient by comparison of 1)a cRQA parameter comparing the breathing data to and the locomotion data(e.g., one or more of the cRQA parameters discussed above) and 2) areference value. In some embodiments, the reference value may be a valueof a cRQA parameter from a reference population. For example, thereference value may be an average value of the cRQA parameter in thereference population. In another example, the reference value may be anaverage value of the cRQA parameter in the reference population, plus orminus one or more standard deviations. In another example, the referencevalue may be a value of the cRQA parameter from the reference populationat a predetermined percentile (e.g., the 90th percentile). The referencepopulation may be, for example, a population without COPD (age-matchedto the patient or not age-matched to the patient), a population withCOPD (age-matched to the patient or not age-matched to the patient), apopulation with COPD of a particular Body mass index, airflowObstruction, Dyspnea, and Exercise (BODE) index, a population with COPDof a particular GOLD Global initiative for chronic Obstructive LungDisease) classification, or any other suitable reference population.

In some embodiments, the reference value may be a previously obtainedvalue of the cRQA parameter from the patient. For example, the referencevalue may be a value of the cRQA parameter obtained from the patient atthe time that the most recent past measurements were taken (e.g., at thelast care provider appointment, or at the previous day). In anotherexample, the reference value may aggregate several previously obtainedvalues of the cRQA parameter from the patient (e.g., the average valueover several past measurement periods, plus or minus one or morestandard deviations).

The estimate generation logic 228 may use the cRQA parameter and thereference value to generate an estimate of COPD severity in the patient.In some embodiments, when the reference value is representative of areference population, the estimate of COPD severity may be an indicationthat the patient's COPD is elevated with respect to a referencepopulation. For example, when the cRQA parameter is percent determinism,entropy, or mean diagonal line length, if the value of the cRQAparameter is greater than a value of the same cRQA parameter for thereference population (e.g., an average, plus an optional one or morestandard deviations), the estimate generation logic 228 may indicatethat the patient's COPD is elevated with respect to the referencepopulation. In another example, when the cRQA parameter is percentrecurrence, if the value of the cRQA parameter is less than a value ofthe same cRQA parameter for the reference population (e.g., an average,plus an optional one or more standard deviations), the estimategeneration logic 228 may indicate that the patient's COPD is elevatedwith respect to the reference population.

In some embodiments, the estimate of COPD severity may be an indicationthat the patient's COPD has increased in severity from a previous time.For example, when the cRQA parameter is percent determinism, entropy, ormean diagonal line length, and the reference value is a previouslyobtained value of the cRQA parameter from the patient (e.g., a singlepreviously obtained value or aggregate of multiple previously obtainedvalues), and the cRQA parameter is greater than the reference value, theestimate generation logic 228 may indicate that the patient's COPD hasincreased in severity. In another example, when the cRQA parameter ispercent recurrence, the reference value is a previously obtained valueof the cRQA parameter from the patient (e.g., a single previouslyobtained value or aggregate of multiple previously obtained values), andthe cRQA parameter is less than the reference value, the estimategeneration logic 228 may indicate that the patient's COPD has increasedin severity.

Returning to FIG. 2, the COPD severity estimation system 200 may includean estimate notification logic 236. The estimate notification logic 236may be coupled to the estimate generation logic 228 and to the I/Odevices 202 (via the I/O device interface 238) and may be configured tonotify the patient or one or more care providers of the COPD severityestimate generated by the COPD severity estimation system 200. In someembodiments, the estimate notification logic 236 may be coupled to thedisplay device 218, and may be configured to cause the estimate of COPDseverity to be displayed on the display device 218. The estimatenotification logic 236 may be configured to cause the display ofadditional information along with the estimate of COPD severity. Thisadditional information may include a BODE index for the patient, a GOLDclassification for the patient, or an EXAcerbations of Chronic pulmonarydisease Tool (EXACT) score for the patient, for example. An illustrativedisplay that may be displayed on the display device 218 upon theinstruction of the estimate notification logic 236 is shown in FIG. 14and discussed below.

In some embodiments, the estimate notification logic 236 may beconfigured to provide a notification of the COPD severity estimate bycausing a textual and/or graphic message to be sent to the patientpersonal computing device 104 and/or the care provider computing device110. This notification may take the form of an e-mail, a text message, asocial media message, a message in a proprietary communication system(such as a messaging system provided by an insurance company orhealthcare system), or any other suitable form. In some embodiments, theestimate notification logic 236 may be configured to provide anotification of the COPD severity estimate to an electronic patientrecord system maintained by a healthcare facility, and the electronicpatient record system may update a patient's file or “chart” with theCOPD severity estimate.

The COPD severity estimation system 200 may include a charging logic230, which may be configured to charge an energy storage device (e.g., abattery) of the wearable computing device 102 when the wearablecomputing device 102 is appropriately coupled to the charging logic 230(e.g., via a cable that supports charging, such as a universal serialbus (USB) cable, or via an inductive or other “wireless” chargingapparatus). When sufficiently charged, the wearable computing device 102may be decoupled from the charging logic 230 and worn by the patient.

The COPD severity estimation system 200 may include heart rate logic232, which may be coupled to the heart rate sensor 210 and may beconfigured to receive a heart rate signal representative of a rate ofheartbeats of the patient. In some such embodiments, the data provisionlogic 226 may be coupled to the heart rate logic 232, and the dataprovision logic 226 may be configured to provide heart rate data, basedon the heart rate signal, to the estimate notification logic 236. Theestimate notification logic 236 may in turn cause the estimate of COPDseverity (generated by the estimate generation logic 228) to bedisplayed simultaneously with the heart rate of the patient (based onthe heart rate data) on the display device 218. The illustrative displayof FIG. 14, discussed in further detail below, includes the patient'sheart rate, for example. The estimate notification logic 236 may notifythe patient and/or care provider of the patient's heart rate inaccordance with any of the embodiments discussed above with reference tonotification of the COPD severity estimate.

The COPD severity estimation system 200 may include a locomotion promptlogic 234, which may be configured to generate a prompt to the patientto begin locomotion. In some embodiments, the locomotion prompt logic234 may be coupled to the locomotion signal receipt logic 224 and may beconfigured to generate a prompt to the patient to begin locomotion upona determination that the patient has not engaged in locomotive activityfor a predetermined period of time. In some embodiments, the locomotionprompt logic 234 may be coupled to clock logic (not shown in FIG. 2) andmay be configured to generate a prompt to the patient to beginlocomotion at a particular time or times (e.g., at 9:00 AM each day).The prompt may take the form of a visual or audio message provided tothe patient personal computing device 104 for display to the patient, ora tactile, visual, or audio message provided to the wearable computingdevice 102 (e.g., a vibration or a tone). In some embodiments, theprompt may include a temporal component that indicates to the patient aperiod of time over which he or she should engage in locomotiveactivity. For example, the wearable computing device 102 may emit a toneat regular intervals that ceases when the patient has moved for longenough for a sufficient locomotion signal to be received by thelocomotion signal receipt logic 224.

As discussed above with reference to FIGS. 1 and 2, the components ofthe COPD severity estimation system 200 may be distributed in anysuitable manner between the computing devices of the computing system100, and the computing devices of the computing system 100 may bethemselves distributed in any suitable manner among sub-computingdevices. FIGS. 3-6 are block diagrams of various arrangements of variouscomponents of the control logic 204 of the COPD severity estimationsystem 200, in accordance with various embodiments.

FIG. 3 depicts an arrangement 300 in which the breathing signal receiptlogic 222, the locomotion signal receipt logic 224, and a communicationdevice 312 are included in a housing 302. The data provision logic 226and a communication device 314 are included in a housing 304 that isdifferent from the housing 302, but the housing 302 and the housing 304may be in communication via the communication devices 312 and 314. Theestimate generation logic 228 and a communication device 316 areincluded in a housing 306 that is different from the housing 302 and thehousing 304, but the housing 304 and the housing 306 may be incommunication via the communication devices 314 and 316. In someembodiments, the housing 302 and the housing 306 may not be in directcommunication, but may communicate via intermediate components in thehousing 304. In some embodiments, the housing 302 and the housing 304may be in communication via a commonly accessible storage device (notshown in FIG. 3, but discussed below with reference to FIG. 9). In someembodiments, the housing 304 and the housing 306 may be in communicationvia a commonly accessible storage device (not shown). The communicationdevices 312, 314, and 316 may be included in, and may take the form ofany of the embodiments of, the communication device 208 of the COPDseverity estimation system 200.

In some embodiments of the arrangement 300 of FIG. 3, the housing 302 isa housing of the wearable computing device 102, the housing 304 is ahousing of the dock computing device 106 or the patient personalcomputing device 104, and the housing 306 is a housing of the remotecomputing device 108 or the care provider computing device 110.

For example, when the housing 304 is a housing of the dock computingdevice 106, the housing 304 may have a connector to receive a matingconnector of the housing 302. In such an embodiment, the breathingsignal receipt logic 222 may be configured to store the breathing signalor breathing data in a storage device (not shown) in the housing 302,the locomotion signal receipt logic 224 may be configured to store thelocomotion signal or locomotion data in a storage device (not shown) inthe housing 302, and when the connectors of the housing 302 and thehousing 304 are mated, the data provision logic 226 may be configured toread the information stored in the storage device(s) of the housing 302.Such an embodiment is discussed in further detail below with referenceto FIG. 4.

In another example, when the housing 304 is a housing of a patientpersonal computing device 104 (e.g., a smartphone), the communicationdevice 312 and the communication device 314 may be wirelesscommunication devices (e.g., short-range wireless communication devices,such as Bluetooth devices). In such an embodiment, the breathing signalreceipt logic 222 may be configured to store the breathing signal orbreathing data in a storage device (not shown) in the housing 302, thelocomotion signal receipt logic 224 may be configured to store thelocomotion signal or locomotion data in a storage device (not shown) inthe housing 302, and when the communication devices 312 and 314 are incommunication, the data provision logic 226 may be configured to readthe information stored in the storage device(s) of the housing 302.

In another example, the communication device 314 and the communicationdevice 316 may be wireless communication devices (e.g., long-rangewireless communication devices, such as cellular devices). In such anembodiment, the data provision logic 226 may provide the breathing dataand the locomotion data to the estimate generation logic 228 wirelessly.In one particular example of such an embodiment, the housing 304 may bea housing of the dock computing device 106, and may wirelessly transmitthe breathing data and the locomotion data to a cloud storage service,where it is accessible by a high-performance computer or other computingdevice instantiating the estimate generation logic 228 (e.g., the remotecomputing device 108 or the care provider computing device 110).

FIG. 4 depicts an arrangement 400 representing a portion of anembodiment of the arrangement 300 of FIG. 3. In FIG. 4, the breathingsignal receipt logic 222, the locomotion signal receipt logic 224, andthe communication device 312 are included in a housing 402, along with astorage device 412 and a communication device 432. The data provisionlogic 226, the charging logic 230, a storage device 414, and acommunication device 434 are included in a housing 404 that is differentfrom the housing 402, but the housing 402 and the housing 404 may be incommunication via the communication devices 432 and 434. The housing 402includes a connector 422 that removably mates with a connector 424 ofthe housing 404. The communication devices 432 and 434 may be includedin, and may take the form of any of the embodiments of, thecommunication device 208 of the COPD severity estimation system 200. Forexample, the communication device 432 may support USB communicationbetween the components of the housings 402 and 404 via the connectors422 and 424. The storage devices 412 and 414 may be included in, and maytake the form of any of the embodiments of, the storage device 206 ofthe COPD severity estimation system 200.

For example, when the housing 404 is a housing of the dock computingdevice 106, the breathing signal receipt logic 222 may be configured tostore the breathing signal or breathing data in the storage device 412in the housing 402, the locomotion signal receipt logic 224 may beconfigured to store the locomotion signal or locomotion data in thestorage device 412 in the housing 402. When the connectors 422 and 424are mated, the data provision logic 226 may be configured to read theinformation stored in the storage device 412 and store that informationin the storage device 414. The charging logic 230 may charge an energystorage device (not shown) in the housing 402 when the connectors 422and 424 are mated. Additionally, the communication device 434 of thehousing 404 may be configured to provide the information stored by thedata provision logic 226 in the storage device 414 to the estimategeneration logic 228 (not shown).

FIG. 5 depicts an arrangement 500 in which the breathing signal receiptlogic 222 is included in a housing 504 having two cables 512 and 528extending therefrom. The connectors 524 and 530 are disposed on thecables 512 and 528, respectively. The connectors 524 and 530 areremovably mateable with the connectors 522 and 526 of a chest strapsensor 502. The chest strap sensor 502 may be included in the breathingsensor 212, and may take any of the forms of the chest strap sensordiscussed above with reference to the breathing sensor 212. When theconnector 522 is mated with the connector 524, the connector 526 ismated with the connector 530, and the chest strap sensor 502 ispositioned around the patient's chest, the breathing signal receiptlogic 222 may be able to receive a breathing signal from use of thechest strap sensor 502.

FIG. 6 depicts an arrangement 600 in which the locomotion signal receiptlogic 224 and an accelerometer 604 are included in a housing 602. Theaccelerometer 604 may be included in the locomotion sensor 214, and maytake any of the forms of the accelerometer discussed above withreference to the locomotion sensor 214. For example, in someembodiments, the accelerometer 604 may be an IMU. The locomotion signalreceipt logic 224 may be coupled to the accelerometer 604 so that thelocomotion signal receipt logic 224 is able to receive a locomotionsignal from the accelerometer 604.

FIG. 7 is a perspective view of an example arrangement 700 of a portionof the COPD severity estimation system 200, in accordance with variousembodiments. In particular, the arrangement 700 may represent anembodiment of the arrangement 500 (FIG. 5) and the arrangement 600 (FIG.6). In FIG. 7, a chest strap sensor 702 has a first end 734, a secondend 736, an interior face 704, and an exterior face 706. When worn by apatient around his or her chest, the interior face 704 is arranged toface the patient's chest. A pocket 712 is disposed on the exterior face706 proximate to the second end 736. The pocket 712 may have twoopenings: a first opening 714 proximate to the second end 736 and asecond opening 716 distal to the second end 736. The pocket 712 may besized to receive a housing 718 in which the breathing signal receiptlogic 222 (not shown) and the locomotion signal receipt logic 224 (notshown) may be disposed. In this manner, the housing 718 may serve asboth the housing 504 (FIG. 5) and the housing 602 (FIG. 6). Inparticular, the housing 718 may include both the breathing signalreceipt logic 222 and the accelerometer 604 (not shown). In someembodiments, the housing 718 may have dimensions less than or equal to 1inch by 2 inches by ½ inch.

A first cable 720 may extend from the housing 718 through the firstopening 714, and a second cable 722 may extend from the housing 718through the second opening 716. The housing 718 and cables 720 and 722may be removable from the pocket 712 and may be oriented in the“opposite” manner in the pocket 712 (i.e., so that the first cable 720extends through the second opening 716 and the second cable 722 extendsthrough the first opening 714). A first connector 724 may be disposed atan end of the first cable 720 opposite the housing 718, and a secondconnector 740 may be disposed at an end of the second cable 722 oppositethe housing 718.

The first connector 724 may be mateable with one of multiple connectors730 disposed on the exterior face 706 of the chest strap sensor 702proximate to the first end 734. The connectors 730 are in electricalcontact with the sensing material of the chest strap sensor 702, andthus provide electrical contact points for measuring a breathing signalusing the chest strap sensor 702. The connectors 730 may be disposed onportions of hook material 728 that both reinforce the chest strap sensor702 around the connectors 730 and may couple with corresponding loopmaterial 726 disposed on the interior face 704 of the chest strap sensor702 proximate to the second end 736. Multiple connectors 730 may enablea patient to adjust the diameter of the chest strap sensor 702 when inuse and mate the first connector 724 with the appropriate one of theconnectors 730 based on the diameter. Additionally, the coupling betweenthe hook material 728 and the loop material 726 during use may providestrain relief to the first cable 720 when the first connector 724 ismated with a connector 730. In some embodiments, the first connector 724may be a female snap, and the connectors 730 may be male snaps. In someembodiments, the first connector 724 and the connectors 730 may becomplementary magnets.

The second connector 740 may be mateable with a connector 732 disposedon the exterior face 706 of the chest strap sensor 702 proximate to thesecond end 736. In some embodiments, the second connector 740 may be afemale snap, and the connector 732 may be a male snap. In someembodiments, the second connector 740 and the connector 732 may becomplementary magnets. The connector 732 is in electrical contact withthe sensing material of the chest strap sensor 702, and thus provides anelectrical contact point for measuring a breathing signal using thechest strap sensor 702. Thus, the arrangement 700 may serve as anembodiment of the arrangement 500 (FIG. 5): the chest strap sensor 702may serve as the chest strap sensor 502, the connector 730 may serve asthe connector 526, the connector 732 may serve as the connector 522, thesecond connector 740 may serve as the connector 524, the second cable722 may serve as the cable 512, the first cable 720 may serve as thecable 528, and the first connector 724 may serve as the connector 530.When the first connector 724 and the second connector 740 are mated withthe connectors 730 and 732, respectively, a closed circuit may beachieved and logic in the housing 718 (e.g., the locomotion signalreceipt logic 224) may be able to receive a locomotion signal using thechest strap sensor 702.

The arrangement 700 may also include a slider 708 formed from a piece offabric or other material wrapped around the chest strap sensor 702. Theslider 708 may include a portion of a loop material 710 arranged on theinterior face 704 of the chest strap sensor 702, and may “slide” alongthe chest strap sensor 702 and be positioned to provide an extra contactpoint with the hook material 728 on the exterior face 706 to furthersecure the chest strap sensor 702 around the patient's chest.

In some embodiments, the housing 718 may be an embodiment of the housing402 of FIG. 4. In particular, the housing 718 may include the breathingsignal receipt logic 222, the locomotion signal receipt logic 224, thestorage device 412, and the communication device 432. The housing 718may also include an additional connector (not shown) to serve as theconnector 422. For example, the housing 718 may include a mini-USBsocket or other connector that can mate with a mini-USB or otherconnector 424 from the housing 404 (which may be, for example, the dockcomputing device 106). In such an embodiment, after the chest strapsensor 702 has been worn and the logic in the housing 718 has receivedthe breathing and locomotion signals (and stored them in the storagedevice 412), the housing 718 may be removed from the pocket 712 and amicro-USB or other cable may be coupled with the connector 422 to “dock”the housing 718 with the housing 404. Operation of this “docked”arrangement may proceed as discussed above with reference to FIG. 4. Inother embodiments, the housing 718 may include the wirelesscommunication device 432 (not shown) to wirelessly transmit informationstored in the storage device 412 to the data provision logic 226 in thehousing 404.

FIG. 8 depicts an arrangement 800 in which the breathing signal receiptlogic 222, the locomotion signal receipt logic 224, the data provisionlogic 226, and a communication device 812 are included in a housing 802.The estimate generation logic 228 and a communication device 814 areincluded in a housing 804 that is different from the housing 802, butthe housing 802 and the housing 804 may be in communication via thecommunication devices 812 and 814. In some embodiments, the housing 802and the housing 804 may be in communication via a commonly accessiblestorage device (not shown in FIG. 8, but discussed below with referenceto FIG. 9). The communication devices 812 and 814 may be included in,and may take the form of any of the embodiments of, the communicationdevice 208 of the COPD severity estimation system 200.

In some embodiments of the arrangement 800 of FIG. 8, the housing 802 isa housing of the wearable computing device 102, and the housing 804 is ahousing of the remote computing device 108 or the care providercomputing device 110.

In one example, the communication device 812 and the communicationdevice 814 may be wireless communication devices (e.g., long-rangewireless communication devices, such as cellular devices). In such anembodiment, the data provision logic 226 may provide the breathing dataand the locomotion data to the estimate generation logic 228 wirelessly.In one particular example of such an embodiment, the housing 802 may bea housing of the wearable computing device 102 and may wirelesslytransmit the breathing data and the locomotion data to a cloud storageservice, where it is accessible by a high-performance computer or othercomputing device instantiating the estimate generation logic 228 (e.g.,the remote computing device 108 or the care provider computing device110).

FIG. 9 depicts an arrangement 900 in which the data provision logic 226and the estimate generation logic 228 are in communication via acommonly accessible storage device 902. The storage device 902 may beincluded in, and may take any of the forms described above withreference to, the storage device 206 of FIG. 2. In some embodiments, thestorage device 902 may be included in a common housing with the dataprovision logic 226, while the estimate generation logic 228 is includedin a different housing. In some embodiments, the storage device 902 maybe included in a common housing with the estimate generation logic 228,while the data provision logic 226 is included in a different housing.In some embodiments, the storage device 902 may be included in a commonhousing with the data provision logic 226 and the estimate generationlogic 228. In some embodiments, the storage device 902 may be includedin a housing different from a housing of the data provision logic 226and a housing of the estimate generation logic 228. For example, thestorage device 902 may be remote from both the data provision logic 226and the estimate generation logic 228 (e.g., in a cloud storage system).

FIG. 10 depicts an arrangement 1000 in which the breathing signalreceipt logic 222, the locomotion signal receipt logic 224, the dataprovision logic 226, and the estimate generation logic 228 are allincluded in a common housing 1002 with a communication device 1004. Upongeneration of the COPD severity estimate by the estimate generationlogic 228, the communication device 1004 may be used to provide the COPDseverity estimate to estimate notification logic 236 (not shown, but maybe included in the housing 1002 or in a different housing).

FIG. 11 is a flow diagram of an illustrative process 1100 for COPDseverity estimation, in accordance with various embodiments. While theoperations of the process 1100 are arranged in a particular order inFIG. 11 and illustrated once each, in various embodiments, one or moreof the operations may be repeated, omitted, or performed out of order.In particular, various operations of the process 1100, althoughillustrated as performed in a particular sequence for the sake ofillustration, may be performed in parallel as suitable. Operations ofthe process 1100 may be described as performed by the COPD severityestimation system 200, as embodied in the computing system 100, forillustrative purposes, but the operations of the process 1100, includingindividual operations of the process 1100, may be performed by anysuitably configured computing device or collection of computing devices.Any of the operations of the process 1100 may be performed in accordancewith any of the embodiments of the computing system 100 and COPDseverity estimation system 200 described herein.

At 1102, the COPD severity estimation system 200 (e.g., the breathingsignal receipt logic 222) may receive a breathing signal. The breathingsignal may be generated through the use of a breathing sensor (e.g., thebreathing sensor 212). The breathing signal may be representative ofbreathing activity of a patient over a time interval.

At 1104, the COPD severity estimation system 200 (e.g., the locomotionsignal receipt logic 224) may receive a locomotion signal. Thelocomotion signal may be generated through the use of a locomotionsensor (e.g., the locomotion sensor 214). The locomotion signal may berepresentative of locomotive activity of the patient over the timeinterval. In some embodiments, the operations discussed with referenceto 1102 and 1104 may be performed at least partially in parallel.

At 1106, the COPD severity estimation system 200 (e.g., the breathingsignal receipt logic 222, the locomotion signal receipt logic 224,and/or the data provision logic 226) may determine whether the receivedbreathing and locomotion signals have adequate characteristics for usein generating a COPD severity estimate. For example, if an inadequatenumber of samples of sufficient locomotive activity have been received,the breathing and/or locomotion sensor is improperly positioned ormalfunctioning, or the signal(s) is contaminated with external noise,the received breathing and locomotion signals may not be adequate foruse in generating a COPD severity estimate.

If the COPD severity estimation system 200 determines at 1106 that thereceived breathing and locomotion signals are not adequate for use ingenerating a COPD severity estimate, the COPD severity estimation system200 may proceed to 1108 and generate a notification to the patient. Thisnotification may include instructions to the patient (e.g., “Please walkat a normal pace for 45 seconds”), describe the detected errorcondition, or simply notify the patient that a COPD severity estimatewill not be generated. The COPD severity estimation system 200 may thenreturn to 1102.

If the COPD severity estimation system 200 determines at 1106 that thereceived breathing and locomotion signals are adequate for use ingenerating a COPD severity estimate, the COPD severity estimation system200 may proceed to 1110 and determine whether one or both of thebreathing and locomotion signals should be up-converted and/ordown-converted so that the two signals are sampled at consistent times.Up- and/or down-conversion may be usefully performed when the breathingsignal and the locomotion signal are sampled at different rates.

If the COPD severity estimation system 200 determines at 1110 thatup-/down-conversion is to be performed, the COPD severity estimationsystem 200 may proceed to 1112 and perform up-/down-conversion. Forexample, in some embodiments, the breathing signal may be adiscrete-time signal having a first sampling rate, and the locomotionsignal may be a discrete-time signal having a second sampling ratedifferent from the first sampling rate. If the first sampling rate ishigher than the second sampling rate, the COPD severity estimationsystem 200 (e.g., the breathing signal receipt logic 222) maydown-convert the breathing signal to match the second sampling rate(e.g., by interpolating the breathing signal and resampling theinterpolated breathing signal at the second sampling rate).Alternatively, if the first sampling rate is higher than the secondsampling rate, the COPD severity estimation system 200 (e.g., thelocomotion signal receipt logic 224) may up-convert the locomotionsignal to match the first sampling rate (e.g., by interpolating thelocomotion signal and resampling the locomotion signal at the firstsampling rate). Analogous operations may be performed if the firstsampling rate is less than the second sampling rate.

The COPD severity estimation system 200 (e.g., the estimate generationlogic 228) may then proceed to 1114 and determine an embedding dimensionfor the breathing and locomotion signals. As noted above, the embeddingdimension determines the size of the vector representation of thebreathing and locomotion signals for cRQA. In some embodiments, the COPDseverity estimation system 200 (e.g., the estimate generation logic 228)may utilize a false nearest neighbors algorithm to determine anappropriate embedding dimension. In other embodiments, any suitableknown technique for determining an appropriate embedding dimension maybe used. In some embodiments, a component of the computing system 100 oran external component may determine an appropriate embedding dimensionin advance (e.g., by applying the false nearest neighbors or otheralgorithm to data similar to the breathing and locomotion data), and thevalue of this embedding dimension may be stored in the storage device206 for access by the estimate generation logic 228; in suchembodiments, the operations of 1114 may not be performed in theillustrated order, or may not be performed by the COPD estimation system200 at all. In some embodiments, an embedding dimension may bedetermined for each COPD severity estimate, while in some embodiments, asame embedding dimension may be used for all COPD severity estimates. Insome embodiments, the embedding dimension may be 5.

The COPD severity estimation system 200 (e.g., the estimate generationlogic 228) may then proceed to 1116 and determine a time delay for theembedding of the breathing and locomotion signals. As noted above, thetime delay determines the spacing between the samples of the signalsthat make up the vector representation of the breathing and locomotionsignals for cRQA. In some embodiments, the COPD severity estimationsystem 200 (e.g., the estimate generation logic 228) may utilize anaverage mutual information algorithm to determine an appropriate timedelay. In other embodiments, any suitable known technique fordetermining an appropriate time delay may be used. In some embodiments,a component of the computing system 100 or an external component maydetermine an appropriate time delay in advance (e.g., by applying theaverage mutual information or other algorithm to data similar to thebreathing and locomotion data), and the value of this time delay may bestored in the storage device 206 for access by the estimate generationlogic 228; in such embodiments, the operations of 1116 may not beperformed in the illustrated order, or may not be performed by the COPDseverity estimation system 200 at all. In some embodiments, a time delaymay be determined for each COPD severity estimate, while in someembodiments, a same time delay may be used for all COPD severityestimates. In some embodiments, the time delay may represent 150-350milliseconds of data. When data is sampled at 60 Hz, for example, thismay represent approximately 10-20 samples.

The COPD severity estimation system 200 (e.g., the estimate generationlogic 228) may then proceed to 1118 and determine a radius for theembedding of the breathing and locomotion signals. As noted above, theradius determines how close a breathing and a locomotion vector have tobe to be counted as a point for cRQA. In some embodiments, the COPDseverity estimation system 200 (e.g., the estimate generation logic 228)may set the radius in order to achieve a particular percent recurrence.For example, the radius may be set to achieve a 2% recurrence. In otherembodiments, the percent recurrence may be selected to be a percentagebetween 1% and 5% (as may be appropriate for behavioral data to achieveadequate sensitivity). In other embodiments, any suitable knowntechnique for determining an appropriate radius may be used. In someembodiments, a component of the computing system 100 or an externalcomponent may determine an appropriate radius in advance (e.g., by usingdata similar to the breathing and locomotion data), and the value ofthis radius may be stored in the storage device 206 for access by theestimate generation logic 228; in such embodiments, the operations of1118 may not be performed in the illustrated order, or may not beperformed by the COPD severity estimation system 200 at all. In someembodiments, a radius may be determined for each COPD severity estimate,while in some embodiments, a same radius may be used for all COPDseverity estimates.

The COPD severity estimation system 200 (e.g., the estimate generationlogic 228) may then proceed to 1120 and may generate cross-recurrencedata comparing the breathing and locomotion data. This cross-recurrencedata may be generated in accordance with the expressions above, usingany suitable computational techniques.

At 1122, the COPD severity estimation system 200 (e.g., the estimategeneration logic 228) may generate one or more parameters of thecross-recurrence data generated at 1120. The parameter(s) generated at1122 may include any of the parameters discussed above, such as percentdeterminism, maximum diagonal line length, mean diagonal line length, orany other suitable parameter.

At 1124, the COPD severity estimation system 200 (e.g., the estimategeneration logic 228) may compare the parameter(s) generated at 1122 tocorresponding reference value(s). The reference value may take the formof any of the reference values discussed above, and the comparison maytake the form of any of the comparisons discussed above.

At 1126, the COPD severity estimation system 200 (e.g., the estimategeneration logic 228) may generate a COPD severity estimate based on thecomparison. This estimate may take the form of any of the estimatesdiscussed above.

At 1128, the COPD severity estimation system 200 (e.g., the estimategeneration logic 228) may store the COPD severity estimate generated at1126 in the storage device 206. The storage of the COPD severityestimate may be substantially permanent or may be temporary (e.g., in abuffer).

At 1130, the COPD severity estimation system 200 (e.g., the estimatenotification logic 236) may provide the COPD severity estimate to a careprovider computing device (e.g., the care provider computing device110). The COPD severity estimate may be displayed on a display device ofthe care provider computing device (e.g., the display device 218) and/orstored on the care provider computing device.

At 1132, the COPD severity estimation system 200 (e.g., the estimatenotification logic 236) may provide the COPD severity estimate to apatient computing device (e.g., the wearable computing device 102 and/orthe patient personal computing device 104). The COPD severity estimatemay be displayed on a display device of the patient computing device(e.g., the display device 218) and/or stored on the patient computingdevice.

Any of the signals, data, parameters, estimates, or other informationused by the COPD severity estimation system 200 during its operation maybe stored in the storage device 206 is any suitable data structure. Forexample, FIG. 13 is a representation of an illustrative data structure1300 for storing data generated and/or used by the COPD severityestimation system 200, in accordance with various embodiments. Theentries and fields of the data structure 1300 are simply illustrative,and various ones of the fields may be omitted or modified, or additionalfields included, as suitable. Additionally, not all fields may bepopulated with data for each entry.

The data structure 1300 may include an entry for each COPD severityestimate for the patient; in FIG. 13, three example entries 1330, 1332,and 1334 are shown. The data structure 1300 may include a date/timefield 1302 that may be used to store a date and/or time at which a COPDseverity estimate for an entry was generated or a date and/or time atwhich the data used for a COPD severity estimate for the entry wascollected. The data structure 1300 may include an embedding dimensionfield 1304 that may be used to store the embedding dimension associatedwith the entry. The data structure 1300 may include a time delay field1306 that may be used to store a time delay for embedding associatedwith the entry. The data structure 1300 may include a radius field 1308that may be used to store a cRQA radius associated with the entry. Thedata structure 1300 may include a percent recurrence field 1310 that maybe used to store a percent recurrence associated with the entry. Thedata structure 1300 may include a percent determinism field 1312 thatmay be used to store a percent determinism associated with the entry.The data structure 1300 may include an entropy field 1314 that may beused to store an entropy associated with the entry. The data structure1300 may include a maximum (diagonal) line length field 1316 that may beused to store a maximum diagonal line length associated with the entry.The data structure 1300 may include a mean (diagonal) line length field1318 that may be used to store a mean diagonal line length associatedwith the entry.

The data structure 1300 may include a frequency ratio field 1320 thatmay be used to store a frequency ratio associated with the entry. Thedata structure 1300 may include a maximum cross-correlation field 1322that may be used to store a maximum value of a cross-correlationassociated with the entry. The data structure 1300 may include a BODEindex field 1324 that may be used to store a BODE index associated withthe entry (e.g., a BODE index determined at approximately the same timeas the cRQA analysis associated with the entry). The data structure 1300may include a GOLD classification field 1326 that may be used to store aGOLD classification associated with the entry (e.g., a GOLDclassification determined at approximately the same time as the cRQAanalysis associated with the entry). The data structure 1300 may includean EXACT score field 1328 that may be used to store an EXACT scoreassociated with the entry (e.g., an EXACT score determined atapproximately the same time as the cRQA analysis associated with theentry).

As noted above, the estimate notification logic 236 may cause thedisplay of a visual representation of a COPD severity estimate (e.g., onthe display device 218). FIG. 14 is a representation of an illustrativedisplay 1400 for displaying data generated and/or used by the COPDseverity estimation system 200, in accordance with various embodiments.The display 1400 may include a cRQA analysis section 1402, which mayinclude plots or other ways of displaying current (and, optionally,past) cRQA parameters associated with the patient. For example, thesection 1402 of FIG. 14 includes a plot 1410 depicting a patient'sgenerated values of percent determinism 1420 over time. The plot 1410also indicates a reference value 1430 against which the values ofpercent determinism 1420 may be compared (e.g., as discussed above withreference to the estimate generation logic 228). The section 1402 ofFIG. 14 includes a plot 1412 depicting a patient's generated values ofpercent recurrence 1422 over time. The plot 1412 also indicates areference value 1432 against which the values of percent recurrence 1422may be compared (e.g., as discussed above with reference to the estimategeneration logic 228). The section 1402 of FIG. 14 includes a plot 1414depicting a patient's generated values of mean diagonal line length 1424over time. The plot 1414 also indicates a reference value 1434 againstwhich the values of mean diagonal line length 1424 may be compared(e.g., as discussed above with reference to the estimate generationlogic 228). In some embodiments, the section 1402 of FIG. 14 may includea short summary of the COPD severity estimation (e.g., “elevated” or“not elevated”) instead of or in addition to the plots 1410, 1412, and1414.

The display 1400 may also include a section 1408 for other COPDindicators, such as the BODE index (shown along with a date ofgeneration associated with the particular BODE index), the GOLD class(shown along with a date of generation associated with the particularGOLD class), and the EXACT score (shown along with a date of generationassociated with the particular EXACT score). The display 1400 may alsoinclude a section 1404 for patient information and a section 1406 forpatient notes. As shown in FIG. 14, the patent information section 1404may provide one or more indicators of the patient's activity level(e.g., number of steps per day, number of bouts of movement per day,percentage of day active, etc.). This information may be provided (inraw or processed form) by the locomotion signal receipt logic 224 orother sensors. Information about the patient's physical activity may berelevant to the care provider, and may be displayed as a graph or otherchart over time as discussed above with reference to section 1402.

Experimental Results Regarding the Effect of Voluntary and InvoluntaryBreathing Rhythms on Locomotor Respiratory Coupling in the Context ofEstimation of COPD Severity

Various embodiments of the COPD severity estimation system 200 (e.g., asinstantiated in the computing system 100) may be used in the studysetting discussed below.

Summary: A study was conducted to investigate the effect of voluntaryand involuntary abnormal breathing rhythms on the ability to couple thelocomotor and respiratory systems. To quantify coupling, three toolswere examined: frequency ratio, cross-correlation, and cRQA. InExperiment 1, twelve young healthy controls were asked to walk at aself-selected speed and breathe either at their normal pace, a fasterpace, or a slower pace to investigate the effect of voluntarily alteringbreathing rhythm on locomotor respiratory coupling. Further, toinvestigate the effect of mismatching frequencies on coupling, the samesubjects were asked to walk at speeds that were +20% or −20% of theirself-selected walking speed but were instructed to either breathe slowerthan normal or faster than normal. Position data from the righttrochanter (sampled at 60 Hertz), right heel (sampled at 60 Hertz), andbreathing data (18 Hertz) were recorded for three minutes and subjectedto data reduction. Breathing data was interpolated to 60 Hertz and allthree data sets were normalized, rendering them unitless. A one-minutesegment (3600 data points) was then selected for analysis for eachsubject. The most common frequency ratio, maximum cross-correlation,cRQA radius, percent recurrence, percent determinism, entropy, maximumline length, and mean line length were calculated. Even though steppingfrequency was constrained to the speed of the treadmill, the healthyyoung subjects were able to couple locomotion to the respiratory rhythmwhen walking at their self-selected pace. This indicates thatbidirectionality exists between the two systems and that this could bedue to a neural/central command driving mechanism. By voluntarilyaltering their breathing rhythm, the respiratory brain centers were ableto coordinate this information regarding the abnormal rhythm with theirlocomotor centers, allowing these subjects to couple under abnormalbreathing conditions without any problems. When subjects were instructedto walk at a slow speed and breathe at a faster rate than normal, thesubjects coupled the two rhythms less often and for shorter lengths oftime as compared with when they were asked to walk at a slow speed andbreathe slowly. In Experiment 2, six patients with COPD and fourteenage-matched controls were tested to investigate the effect ofinvoluntary, abnormal breathing patterns on locomotor respiratorycoupling. As discussed above, patients with COPD represent a populationthat has abnormal breathing patterns due to the pathophysiology of thedisease. Patients with COPD and age-matched controls were asked to walkon a treadmill for four minutes under a self-selected walking speed andspeeds +/−10% and +/−20% of their self-selected speed. No instructionsregarding breathing were given. Data treatment and extraction of thedependent variables were the same as in Experiment 1. Comparison of meandependent variables was conducted between the two groups and between thefive speeds. Additionally, to investigate the effect of age and diseaseon the dependent variables, patients with COPD, age-matched controls,and the healthy young adults (from Experiment 1) were compared duringtheir self-selected walking paces. Patients with COPD demonstrated astricter range of frequency ratios that represented more rigid and lesscomplex coupling as well as demonstrating stronger temporal similarityand increased coupling strength. When patients with COPD were comparedwith young adults and age-matched controls at their self-selectedspeeds, patients with COPD demonstrated an increased number of times inwhich they coupled the two rhythms, and for different lengths of time,as compared with their age-matched controls. This suggests that patientswith COPD demonstrate more rigid coupling, due to the abnormal breathingrhythms. Changes in speed elicited changes in cRQA percent determinism,entropy, maximum line length, and mean line length in both groups,demonstrating that coupling is in fact altered under different taskdemands. However, the majority of the adaptations to speed were elicitedduring the slowest walking speed, and coupling was tightest in patientswith COPD. Furthermore, a significant interaction for entropy was foundin which patients with COPD were able to couple the two rhythms togethermore often and for different lengths of time. From a dynamic systemstheory point of view, preferred performance in certain situations can bealong a continuum of very rigid to very random and patients with COPDappear to demonstrate a very rigid coupling pattern. They prefer a 1:1or 2:1 coupling that is much tighter than their healthy counterparts,and it is very difficult for them to phase-shift into another behavioralstate and a stronger coupling from cRQA results. Rehabilitation programsshould be designed to utilize control parameters to try to move thebehavioral attractor from this very rigid pattern into something morecomplex (e.g., 3:1 or 4:1). The cRQA tools studied in these experimentsappear to be effective in estimating locomotor respiratory coupling andproved to be more sensitive to changes in condition, group, and speedrelative to the most common frequency ratio and cross-correlation. Thesetwo combined experiments demonstrated that changes in voluntary andinvoluntary abnormal breathing rhythms can alter one's ability to couplehis or her locomotor and respiratory system, and more so in involuntaryabnormalities than voluntary abnormalities. Healthy young adultsdemonstrate more flexibility, and this may have assisted them inadapting to the task demands. Furthermore, involuntary abnormalbreathing patterns have a significant influence on coupling.

Introduction: In some biological systems, there may be a mutualattraction between two rhythms, and eventually the rhythms will entrainto the intrinsic rate of the other. This natural desire to entrain isresisted by the rhythms' own intrinsic rates and may result in complexcoupling between the two rhythms. In line with this concept ofentrainment, coupling between movement and respiration in mammals,including humans, may be present. Coupling between locomotion andrespiration can be lost through disease, for instance, in Parkinson'sdisease.

Three main hypotheses have been proposed that would elicit suchentrainment between locomotion and respiration: 1) chemoreception, 2)central command, and 3) peripheral feedback mechanisms. Thechemoreception hypothesis stipulates that carotid body receptors detectgas partial pressure changes or other metabolic changes and/or theeffective value of oxygen concentration in the lungs influencerespiratory control centers. The central command hypothesis specifiesthat motor efferent projections either directly or indirectly connect torespiratory centers in the brain stem. The peripheral feedbackmechanisms hypothesis suggests that somatic afferents from the skeletalmuscle tissue project to the respiratory control centers as well,including influences such as forces acting on the trunk and thorax andflexion and extension movements of the axial skeleton. Existing evidencein humans seems to lean toward a neural-mechanical influence more sothan a metabolic influence on coupling between locomotion andrespiration, and some researchers believe that the coupling between thecardiac and locomotor systems elicits a strong influence on the couplingbetween respiration and locomotion. The majority of existing models forthe locomotion-respiration relationship have been criticized as beingoverly simplistic given the complexity of the relationship betweenmovement and respiration.

Coupling between two or more biorhythms could also be the result ofbidirectional information processing. In humans, existing evidencesuggests that locomotion drives respiration, respiration driveslocomotion, and that their influence on each other is bidirectional.However, it is not understood how voluntary control of respirationinfluences the entrainment to locomotion. Respiration is one of the fewautonomic processes of the human body that can also be controlledthrough voluntary control. Asking an individual to force his or hernatural breathing outside of a comfortable rhythm (i.e., breathe sloweror faster than normal) without instructions regarding step pattern mayin fact cause a disruption in the entrainment to locomotion.Furthermore, perturbations or speed changes that alter movementfrequency may influence breathing frequency. Therefore, constrainingstepping frequency to a rate that is outside of a person's comfortablepace may also exert alterations in breathing entrainment. Previousresearch has not adequately explored cognitive control of breathing andabnormal frequency of walking.

Although not of voluntary control, it was previously unknown howinvoluntary, abnormal breathing rhythms affect entrainment. That is, howdoes having poor respiration or abnormal lung function affectentrainment? As discussed above, COPD is defined in terms of fixedairflow limitation. Patients with COPD and asthmatics with severe airwayobstruction demonstrate abnormal breathing rhythms, but previousresearch has demonstrated a not-significant loss in long-term dependencein COPD patients from one breath to the next (i.e., Brownian motion) ascompared with asthmatics and age-matched controls (i.e., pink noise). Aloss in memory or long-term dependence of breathing may be associatedwith aging, but it appears that the pathophysiology of COPD may causeeven more degradation to long-term dependencies. Few studies haveattempted to understand the role of altered breathing rhythms onentrainment, and those that have done something similar have done so byhaving healthy individuals breathe to an external stimulus or byinstructing them to maintain a certain breath to movement frequency.

An aspect of coupling may be a resistance to perturbations. The mostcommon way to test coordination is to perturb one or both of therhythms. Speed is commonly used as a rehabilitation parameter. Typicallyin rehabilitation settings, patients are asked to increase or decreasewalking speed.

The results of this study included improved approaches to quantifyingand classifying the effect of 1) voluntarily altering breathing rhythmsoutside of one's preferred frequency on entrainment with locomotion and2) an involuntary, abnormal breathing rhythm on locomotor respiratorycoupling. In addition, the effect of speed perturbations was explored.

Experiment 1 included an investigation of the effects of voluntarilybreathing slower or faster than normal under self-selected walkingspeeds, as well as faster and slower walking speeds, in healthy youngadults.

Experiment 2 included an investigation of the effects of an involuntary,abnormal breathing rhythm on locomotor respiratory coupling. Patientswith COPD were compared with age-matched controls.

In both experiments, coupling was investigated using several tools.First, an integer ratio was calculated for the number of heel strikesper breath. This technique has been used extensively within thelocomotor respiratory coupling literature, and a 2:1 ratio waspreviously found to be the dominant ratio. Lower-order frequency ratios,such as a 1:1 or 2:1, have been interpreted as a “simple ratio” asopposed to a higher-order ratio, such as 3:1 or 3:2.

Cross-correlation was utilized to measure the degree of temporalsimilarity between two rhythms. Cross-correlation has been utilizedpreviously to describe locomotor respiratory coupling.

Additionally, cRQA techniques were utilized to quantify coupling betweenthe walking and breathing rhythms. As discussed above, unlike standardRQA, cRQA explores recurrences between two different data sets, ratherthan within one data set.

Experiment 1: Twelve healthy young adults ages 19 to 35 years old wererecruited through word of mouth and participated in this study (Table1). Subjects were included if they did not have a history of injuryand/or disease that would affect their gait walking patterns.

Upon inclusion in the study, patients were asked to change into aform-fitting suit, and under the suit, they were asked to wear awireless physiological monitor (BIOHARNESS 3; Zephyr Technology Corp.,Annapolis, Md.; 18 Hz) against their skin to measure chest movement (asa surrogate for breathing). The metallic material on the physiologicalmonitor's harness was dampened with water before placing the strapsagainst the skin. The investigator confirmed that the straps and harnesswould not slip during data collection, ensuring a tight fit. Reflectivemarkers were placed on anatomical locations, bilaterally, according to amodified Helen Hayes marker set. Subjects were asked to choose aself-selected speed on the treadmill. A self-selected pace was definedfor the subjects as a comfortable walking speed, a pace that they wouldwalk from their vehicle into the building or across the campus. In orderto choose their speed, subjects were allowed to control the speed of thetreadmill, and once they mentioned they felt they had reached acomfortable walking speed, the investigator increased the speedslightly. The speed was increased until the subject reported that thetreadmill was moving faster than they were comfortable walking. Thetreadmill was then slowed again in phases until subjects mentioned thatit felt too slow. These steps were repeated until a speed was found thatwas a comfortable walking pace for the subject. This process tookanywhere from five to ten minutes for most subjects.

Once a subject's comfortable walking speed was chosen, the subject wasallowed to rest for a minimum of one minute. After the subject was wellrested, he or she returned to the treadmill to complete three and a halfminutes of walking on the treadmill at the self-selected pace with noparticular instructions regarding the subject's breathing pace (NORMSELF). Three dimensional marker positions from the last three minutes ofwalking were recorded with a high-speed motion capture system (MotionAnalysis Corp., Santa Rosa, Calif.; 60 Hz). Breathing data from thephysiological monitor was synchronized and recorded simultaneously withthe three dimensional marker positions via a multifunction dataacquisition USB (NI USB-6218; National Instruments Corp., Austin, Tex.)and a custom LabVIEW program (National Instruments Corp., Austin, Tex.).

After completion of the NORM SELF condition, subjects were then asked towalk for three and a half minutes under six more, randomly assigned,conditions. The conditions were:

-   -   Walking at their self-selected pace but were asked to breathe        slower than they are accustomed to doing (SLOW SELF). These same        breathing instructions were given for each of the other two SLOW        conditions.    -   Walking at their self-selected pace but were asked to breathe        faster than they normally would (FAST SELF). The instructions        were to breathe at a faster rate but not to the point in which        they felt that they were hyperventilating or not getting enough        air. These same breathing instructions were given for each of        the other two FAST conditions as well.    -   Walking at a speed +20% faster than their self-selected pace and        breathing slower than normal (SLOW +20%).    -   Walking at a speed +20% faster than their self-selected pace and        breathing faster than normal (FAST +20%).    -   Walking at a speed −20% slower than their self-selected pace and        breathing slower than normal (SLOW −20%).    -   Walking at a speed −20% slower than their self-selected pace and        breathing faster than normal (FAST −20%).

A minimum of one minute of rest was given between each trial to preventthe onset of fatigue.

Each three-minute time series of marker position data and/or breathingdata was then subjected to data reduction to calculate the dependentvariables. A custom Matlab program (Matlab 2007, Mathworks, Inc.,Concord, Mass.) was written to load the sagittal right trochanter dataand right heel data along with the breathing data from each condition.The trochanter data set was chosen as it represents of the globalvertical movement of the body during walking and was used in analysis ofcross-correlation and cRQA. The coupling of breathing with the sacral orheel marker were also explored, but the movements of these markers wereeither too complex or too much off plane to truly represent the actualmotion of walking. The heel data set was utilized to calculate frequencyratio.

Breathing data were plotted for spikes and outliers. Spikes and datapoints greater than three standard deviations from the mean wereremoved. A cubic spline was used to interpolate the removed data points.The breathing data were then interpolated to 60 Hz using a cubic spline.Breathing, trochanter, and heel data were normalized to the unit vectorand, hence, rendered all data sets unitless. Finally, a one-minutesegment of the trial was selected for analysis (3600 data points). FIG.15 includes graphs 1502 and 1504 illustrating representative segmentselection for two healthy young adults, respectively, from the NORM SELFcondition. Selected one-minute segments from the data sets were used fordata analysis. All locomotion data were visually inspected and no spikesor outliers were found in the data of FIG. 15. On the other hand, therespiration data were prone to spikes and outliers 1506. The normalizeddata, with the spikes and outliers 1506 removed, were plotted (1508) anda 3600 data point segment of the entire trial for each participant wasselected for analysis (1510). Segments were selected as the first3600-length segment available that had minimal treatment as far asremoving outliers and spikes. Due to the nature of the wirelesstechnology and sometimes due to the metallic material on thephysiological monitor's harness not being wet enough, the breathingsignal experienced some dropout for some conditions. Almost all trialshad a minimum of 3600 data points that were not corrupted by spikes. The3600 data points that were chosen for analysis belonged to the firstavailable one-minute segment in the time series (as discussed above withreference to FIG. 15). Therefore, for a completely uncorrupted file, thefirst 3600 points were used.

The same selected segment of breathing, trochanter, and heel data werethen filtered and subjected to the following analysis for thecalculation of dependent variables: frequency ratio, cross-correlation,and cRQA. The breathing data was filtered using a 10th order Butterworthfilter at 0.5 Hz, and the trochanter and heel data sets were filteredusing a 10th order Butterworth filter at 10 Hz. FIG. 16 includes graphsillustrating filtered breathing 1602 and locomotion (marker position attrochanter 1604 and heel 1606) data from a representative healthy youngadult for one minute of walking during the NORM SELF condition. All datawere normalized between 0 and 1. Breathing data were filtered using acutoff frequency of 0.5 Hz, and all three of the marker position datasets were filtered using a cutoff frequency of 10 Hz.

A custom Matlab program was utilized to calculate the ratio of heelstrikes to breaths. The presence of a heel strike was calculated as thelocal maximum of the derivative of the heel position data. Usingexisting methods, the discrete relative phase was calculated between thebreathing data and the timing of heel strikes. Based on the discreterelative phase value, the number of heel strikes within a breath can becalculated. For instance, if the discrete relative phase is between 0°and 360°, there was one heel strike. However, if the returned value isbetween 360° and 720°, there were two heel strikes, and so forth.

For each breath taken during the one-minute data segment, the number ofheel strikes was calculated. From there, the ratios (e.g., 1:1, 2:1, . .. , 11:1) were counted for each trial. The most utilized ratio wasreported in addition to the percentage of that ratio being used. If morethan one ratio was found to be the most commonly used, visual inspectionwas performed to see which ratio was repeated within the mostconsecutive breaths. This would represent a stronger coupling to thatratio, and therefore that was the ratio defined as the most common.Lastly, the total number of breaths and total number of heel strikes wasrecorded to confirm that the subjects followed instructions regardingthe breathing rate and that they adapted to the speed alterations of thetreadmill.

Using the Matlab function crosscorr, the breathing and trochanter datasets were used to determine the maximum cross-correlation value.Cross-correlation may be calculated as discussed above. Strengths ofcorrelations were defined as follows: ±1.00 to 0.80=very strong; ±0.79to 0.60=strong; ±0.59 to 0.40=moderate; ±0.39 to 0.20=weak; ±0.19 to0=no relationship.

In order to perform cRQA, all time signals underwent analysis for timedelay and embedding dimension. In order to find the time delay, theaverage mutual information algorithm was used. Average mutualinformation was run for each trial, providing a time delay specific forthat trial. The mean and median time delays were then calculated foreach condition. After inspecting the range of time delays for eachcondition, the mean time delay was chosen for all trials within thatcondition. The average time delays ranged from 12 to 16 for allconditions. Embedding dimension was determined using the false nearestneighbor algorithm. Again, embedding dimensions were inspected for eachcondition. Overall, the majority of all trials elicited an embeddingdimension of five. Specifically, the range of embedding dimensions wasfrom 5 to 7 with over 90% of all trials requiring an embedding dimensionof 5. All trials were unfolded into 5 dimensions.

Using custom Matlab codes, cRQA was performed on the breathing andtrochanter data. Using the input values of the time delay and embeddingdimension, all attractors were unfolded into their appropriate statespace, creating new time-delay vectors. The Euclidean distance betweeneach data point in each vector was then calculated and stored into amatrix. The distances were rescaled to maximum distance.

A recurrence plot was then derived from the distance matrix by thedetermined radius threshold. For example, FIG. 17 is a recurrence plotof breathing and locomotion (trochanter) data for a healthy youngsubject, FIG. 18 is a recurrence plot of analogous data for an older,age-matched control, and FIG. 19 is a recurrence plot of analogous datafor a COPD patient, with each accompanied by the associated time series.As discussed above, distances within the distance matrix that were equalto or less than the radius were defined as recurrent at coordinates iand j. If the point i, j was determined as recurrent, then it appearedas a point on the recurrence plot. Therefore, the recurrence plot was atwo-dimensional representation of points having a distance within thethreshold of the radius. To set the radius for each trial, a radius wascalculated that would provide for a 2% recurrence threshold. Percentrecurrence, as discussed above, was defined as the number of recurrentpoints divided by the total number of possible recurrent points times100. To achieve adequate sensitivity, the percent recurrence may be setat 1-5% for behavioral data. In FIGS. 17-19, the normalized and filteredbreath time series is plotted along the X axis and the normalized andfiltered locomotion series is plotted along the Y axis. All 3600 datapoints are unfolded into their state space and the Euclidean distance iscalculated for each i, j coordinate. An i, j point on the recurrenceplot that is indicated by a spot represents a Euclidean distance betweenthe breathing and locomotion data that was equal to or less than theradius. Therefore, the recurrence plot is a two-dimensionalrepresentation of points having a distance is within the threshold ofthe radius. From this plot, the percent recurrence, entropy, maximumlength of line, mean line length, and percent determinism can becalculated. Note that a graphical representation of the recurrence plotneed not to be generated to generate the cRQA parameters discussedherein; instead, these parameters may be generated using some or all ofthe recurrence data that could also be used to generate a recurrenceplot.

Analysis of the recurrence plot was then completed. A priori, it wasdetermined that a minimum of six points in a row would be counted as adiagonal line. Six points was chosen because this represented 100milliseconds of data, and any coupling happening under this time frame,for the purposes of this investigation, would be treated as spuriousand/or due to reflex movement. The use of a minimum of 100 millisecondsof data in the definition of a diagonal line may also account forcorticospinal latency. Once all lines longer than six consecutive pointswere counted, the length of the maximum line was determined and the meanlength of the lines was calculated. The maximum length of a line mayrepresent of the strength of the coupling between the attractors orrhythms. The mean line length may represent the average length of timethat the two data sets are coupled.

The Shannon entropy was then calculated based on the distribution ofdiagonal line lengths, not for the time series, as discussed above.

Finally, percent determinism was calculated, as discussed above. Intotal, the dependent variables extracted from the cRQA analyses wereradius, percent recurrence, the mean line length, maximum line length,the entropy of line lengths, and percent determinism.

The mean for each condition was calculated for most common frequencyratio, total number of breaths, total number of heel strikes, andmaximum cross-correlation. In addition, the condition means from thedependent variables under the cRQA analysis were calculated. A repeatedmeasures Analysis of Variance (ANOVA) was conducted to identifydifferences between the seven conditions (NORM SELF, SLOW SELF, FASTSELF, SLOW −20%, FAST −20%, SLOW +20%, FAST +20%) for the dependentvariables listed above. Analysis was performed in SPSS (SPSS 20.0, IBM,Armonk, N.Y.). Normality was examined for each dependent variable. TheGreenhouse-Geisser correction was reported for all analyses that did notmeet normality requirements. Tukey's post hoc comparisons were used if asignificant main effect was found. The significance level was set at0.05.

Results of Experiment 1: A significant main effect of condition wasfound for both total number of breaths (F_(6,54):3.3, p=0.01) and totalnumber of heel strikes (F_(6,54):10.1, p<0.001). This confirms that thesubjects did in fact change their breathing patterns during theconditions as they were asked to do and adapted to changes in thetreadmill speed. Through visual inspection, it can be seen that the mostcommon frequency ratio was not altered even though the total number ofbreaths was different (Table 2). However, it is difficult to analyzethis statistically, and therefore, the number of heel strikes from themost common ratio was analyzed and a significant main effect ofcondition was found (F_(6,54):2.9, p=0.02). It was found that more heelstrikes per breath were taken in the SLOW +20% condition as comparedwith the NORM SELF condition (p=0.01). In addition, the FAST −20%condition elicited significantly less heel strikes than the SLOW +20%condition (p=0.001).

The maximum cross-correlation elicited a significant main effect forcondition (F_(6,54):7.0, p=0.004) (Table 3). For the breath and thetrochanter data sets, the maximum cross-correlation value was notsignificantly different between any of the self-selected paceconditions. However, differences were found for conditions involvingchanges in speed. The maximum cross-correlation value was significantlydecreased in the SLOW +20% (p=0.01) and the FAST +20% (p=0.01) ascompared with the NORM SELF condition. As compared with the SLOW SELFand FAST SELF, a significant decrease at SLOW +20% (p=0.001 and p=0.03,respectively) and FAST +20% (p=0.04 and p=0.04, respectively) was found.In addition, FAST −20% had a significantly increased maximumcross-correlation value as compared with SLOW SELF and FAST SELF as well(p=0.01 and p=0.03, respectively). Further, FAST −20% had also asignificantly increased maximum cross-correlation value as compared withSLOW +20%, FAST +20% and SLOW −20% (p=0.003, p=0.01 and p=0.004,respectively).

For the breath and trochanter data sets, no main effect of condition wasfound for radius or percent recurrence (Table 4). On the other hand, amain effect of condition was found for percent determinism(F_(6,54):2.8, p=0.02), entropy (F_(6,54):2.6, p=0.03), maximum line(F_(6,54):5.9, p<0.001), and mean line (F_(6,54):2.7, p=0.02). Percentdeterminism was significantly increased in the SLOW −20% as comparedwith SLOW SELF (p=0.03), FAST SELF (p=0.003), FAST +20% (p=0.02), andFAST −20% (p=0.02). Further, percent determinism was also significantlyincreased in FAST −20% as compared with FAST SELF (p=0.02). For entropy,SLOW +20% was significantly lower as compared with NORM SELF, FAST SELF,SLOW −20%, and FAST −20% (p=0.03, p=0.02, p=0.03, and p=0.02,respectively). For maximum line, SLOW −20% was significantly longer ascompared with SLOW +20% (p=0.01). In addition, FAST −20% had asignificantly longer maximum line as compared with NORM SELF (p=0.003),SLOW SELF (p=0.02), FAST SELF (p=0.01), and SLOW +20% (p=0.001). BothSLOW −20% and FAST −20% demonstrated significantly longer mean lines ascompared with SLOW +20% (p=0.01 and p=0.03, respectively).

One purpose of Experiment 1 was to investigate the effect of voluntarilyconstricting breathing rates to either slower or faster than one'spreferred rate on locomotor respiratory coupling. No significantdifferences were found between the NORM SELF, SLOW SELF, and/or FASTSELF conditions, indicating that voluntary alteration of breathing doesnot influence locomotor respiratory coupling. This provides support forbidirectionality between the two systems.

At least one previous study has indicated that using paced breathingcould significantly increase coupling between running and breathing. Thepaced breathing was done by having the subjects breath to an acousticsignal that was triggered by the leg movement, thus constraining thesubjects to breathe in pace with the leg movement. This explains why theuse of paced breathing may have increased coupling. Another recent studydemonstrated that using an auditory stimulus to synchronize breathingwith pedaling during cycling worked better when subjects were instructedto pedal to the stimulus versus breathe in time with the stimulus. Evenso, these authors did find that pacing the breathing rate to theauditory stimulus did have some effect on the coupling between breathingand cycling.

The investigation described above differs significantly from these twostudies in that subjects were not constrained to breathe in time withtheir walking pace and/or breathe in time with a stimulus. In fact, themethodology was designed to discourage this type of strategy. Subjectswere allowed to voluntarily control their breathing rate. The treadmillconstricted the stepping frequency, and subjects were instructed to tryto breathe either faster or slower than they normally would. This designwas implemented to test the bidirectionality of respiratory action onthe locomotor system and vice versa. The current results do support thehypothesis that bidirectionality exists between the two systems. Eventhough subjects walked on a treadmill, which inherently limits thevariability allowed in stepping frequency, the healthy young subjectswere able to still couple locomotion to the respiratory rhythm.

In addition, this provides further evidence for a neural/central commanddriving hypothesis. First, brain stem controls to respiration have beenclearly identified: the medial parabrachial/Kölliker Fuse complex in thedorsal pons regulates the phase switching between inspiration andexpiration, and the pre-Bötzinger complex in the ventrolateral medullafunctions as the respiratory rhythmogenesis hub. Second, through theadvancement of brain imaging, it is known that supraspinal control isrequired during walking, not just a central pattern generator. Inaddition, in animal models, it has been shown that signals from thecervical and lumbar limb afferent inputs are conveyed to the brain stemrespiratory centers, including the parabrachial/Kölliker Fuse complex.Additionally, the mesencephalic locomotor region, has been shown to havedorsal projections to the respiratory control centers and ventralprojections to the reticulospinal tract (locomotion). Although these twofindings have been identified in animals, there is translation to thehuman brain. Some researchers have shown that the organization ofsupraspinal locomotor centers identified in quadrupedal animals has beenpreserved with bipedal locomotion, providing further support for aneural control mechanism over locomotor respiratory coupling.

A secondary aim of this study was to investigate the effect ofmismatching frequencies on coupling. For example, if subjects arewalking faster, they may naturally want to breathe faster, butinstructions were given to constrain their breathing to a slower rate.Partial support was found for the proposed hypothesis that alterationsin speed that did not match the breathing frequency (e.g., SLOW +20% andFAST −20%) would cause a greater disruption to entrainment as opposed tothe two matching frequencies (e.g., SLOW −20% and FAST +20%). The SLOW−20%, in which subjects walked slower and were instructed to breatheslowly, demonstrated significantly more patterns in line lengths andstronger coupling between the two rhythms, and the average length oftime that the two data sets were coupled was longer as compared with theSLOW +20% condition. Subjectively, subjects commented that the SLOW +20%was “weird” and that they “had to really think about it.” One subjectcommented that a “couple of times I forgot about it and had to come backto it.” Still another mentioned that he had “conscious control,breathing would not match walking.” It is possible that instructingsubjects to breathe slowly while having them walk faster than theirself-selected walking pace was the hardest condition presented to them.

If this was the case, it could be expected that having the subject walkslower than the self-selected pace, but breathe faster than normal,would also be difficult. Only one difference was found for the fastbreathing mismatch conditions, the maximum cross-correlation value. Inthe FAST −20%, the maximum cross-correlation was found to besignificantly greater than during the FAST +20%. This indicates that thestrength of temporal similarity was coupled stronger during the mismatchcondition; however, upon a closer look, the correlation values are 0.259for FAST −20% and 0.113 for FAST +20%. Although significant, thesecorrelation values are hardly meaningful. They are weak to norelationship, indicating that there may truly be no difference incoupling at the fastest walking pace. While preferred coupling may alteras speed, frequency, or workload is increased, this may not always bethe case. There may be limits in which coupling can be achieved, andoutside of these limits, movement no longer has an influence on thebreathing rate.

The 20% increase in walking speed could also contribute to the lack offindings in the faster conditions. The subject population studied is ayoung, healthy, active group of subjects that may be used to navigatingcollege campuses in which walking faster is not out of the ordinary. Onthe other hand, walking slower may not be something that they encounteron as frequent of a basis, thus explaining why the slower conditionelicited changes in coupling, whereas the faster walking conditions didnot. There may be a speed that elicits a breakpoint in locomotorrespiratory coupling. This concept has been utilized by previousresearchers to determine anaerobic threshold while cycling, in which itwas confirmed that a point in which locomotor and respiratory rhythmsare no longer coupled predicts anaerobic threshold.

Experiment 2: A total of six patients with COPD and 14 healthyage-matched controls were recruited and consented to participate in thisstudy. COPD was determined based on reported previous diagnosis of thedisease and confirmed with spirometry testing ratio of forced expiratoryvolume in one second to forced vital capacity (FEV₁/FVC) of less than0.7. Spirometry testing was completed without a bronchodilator.Participants were excluded if they had a history of back or lowerextremity injury or surgery that affected the subject's mobility or anyother process limiting the ability to walk, including neurologicaldisease or impairment.

Data collection procedures were the same as Experiment 1, except for thewalking conditions. Both patients with COPD and healthy age-matchedcontrols underwent the selection of their self-selected pace (SELF). Theprocedure was the same as Experiment 1. Once a subject's comfortablewalking speed was chosen, he or she was allowed to rest for a minimum ofone minute and then returned to the treadmill to complete four minutesof walking at that chosen speed. No instructions were given regardingbreathing for any of the trials. Three-dimensional marker positions andbreathing data for four minutes were recorded in a similar fashion toExperiment 1. After completion of the self-selected pace condition,subjects were then asked to walk for four minutes under four more,randomly assigned, conditions. The conditions were +/−10% and +/−20% oftheir self-selected pace.

Data treatment was the same as from Experiment 1, and the same dependentvariables as in Experiment 1 were calculated from each trial. Thisincluded the most common frequency ratio, total number of breaths, totalnumber of heel strikes, maximum cross-correlation, cRQA radius, percentrecurrence, mean line length, maximum line length, entropy, and percentdeterminism. The mean for each group and speed was calculated for eachdependent variable. A 2×5 repeated measures ANOVA was conducted in SPSS(SPSS 20.0, IBM, Armonk, N.Y.) to determine the effect of group(patients with COPD and healthy age-matched controls) and speed (SELF,−10%, +10%, −20%, +20%) on each dependent variable. Normality wasexamined for each dependent variable. The Greenhouse-Geisser correctionwas reported for all analyses that did not meet normality requirements.Tukey's post hoc comparisons were used if a significant main effect ofspeed was found. In addition, to explore the effect of age and diseaseon the dependent variables, a 1×3 one-way ANOVA was used to comparehealthy young adults (NORM SELF from Experiment 1), older age-matchedadults, and patients with COPD during the SELF condition. Thesignificance level was set at p<0.05.

Results of Experiment 2—Patients with COPD versus age-matched controls:A significant main effect of group (F_(1,44):5.7, p=0.03) and speed(F_(4,56):25.9, p<0.001) was found for the total number of heel strikes(Table 5). This result was anticipated as the walking speed in thepatients with COPD was significantly decreased (Table 1). No interactionwas found for the number of heel strikes. No main effect of group orspeed and no interaction were found for number of breaths and the mostcommon number of heel strikes per breath (Table 5). The most commonfrequency ratio utilized by both groups was a 2:1 strategy; however, therange of ratios commonly used differed between the two groups. Thepatients with COPD utilized a 1:1 ratio that was not found to be usedcommonly by the age-matched controls. The age-matched controlsdemonstrated a wider range of ratios that they commonly used within thetrials.

A main effect of group was found for the maximum cross-correlation value(F_(1,15):5.9, p=0.03). The patients with COPD demonstrated asignificantly greater maximum cross-correlation value as compared withtheir age-matched controls (Table 6). Neither a main effect of speed noran interaction was found for maximum cross-correlation values.

No significant main effect of group, speed, and interaction was foundfor radius and percent recurrence. This confirms that the parametersused to determine percent determinism, entropy, maximum line length, andmean line length were the same for both groups at all speeds (Table 7).A main effect of group was found for maximum line length (F_(1,15):6.5,p=0.02). Patients with COPD demonstrated a longer maximum line lengththan did the age-matched controls. Percent determinism, entropy, andmean line lengths did not elicit a main effect of group.

A main effect of speed was found for percent determinism (F_(4,60):4.6,p=0.003), entropy (F_(4,60):6.5, p=0.002), maximum line length(F_(4,60):4.1, p=0.01), and mean line length (F_(4,60):5.4, p=0.01). Forpercent determinism, −20% was significantly greater as compared withSELF (p=0.009), −10% (p=0.02), +10% (p=0.001), and +20% (p=0.002).Entropy was significantly decreased during +10% speed as compared withSELF (p=0.03) and −20% (p<0.001). The −20% speed elicited significantlyincreased entropy as compared with −10% and +20% (p=0.03 and p=0.001,respectively). The maximum line length was shorter at +10% as comparedwith SELF (p=0.04), −10% (p=0.02), and −20% (p=0.02). In addition, themaximum line length at +20% was found to be significantly shorter thanat −20% (p=0.02). Similar to the maximum line length, the mean of linelengths was shorter at +10% as compared with SELF, −10%, and −20%(p=0.01 for all three comparisons). In addition, the maximum line lengthat +20% was found to be significantly shorter than at −20% (p=0.01).

One significant interaction was found for entropy (F_(4,60):3.2,p=0.04). FIG. 20 illustrates the interaction of entropy between thebreath and locomotion (trochanter) time series in patients with COPDcompared with the age-matched controls. In FIG. 20, the followingsymbols represent significant difference from (‡) SELF, (#) −10%,(@)+10%, and (̂) −20%. The patients with COPD demonstrated decreasedentropy at the fastest speeds; whereas the age-matched controlsdemonstrated lower entropy except at −20% where it was significantlyincreased, similar to the patients with COPD. No interaction was foundfor percent determinism, maximum line length, and the mean of linelengths.

Results of Experiment 2—Patients with COPD versus age-matched controlsversus young healthy controls: Although 2:1 was the most common ratioused by all three groups, the range of ratios utilized was differentbetween the groups. The patients with COPD were restricted in the ratiothat they preferred to use, whereas the age-matched controls used aslightly larger range of ratios. Moreover, the healthy young adults usedthe widest range of ratios, from 2:1 to 9:1. The most common number ofheel strikes in one breath was not significantly different betweengroups.

The maximum cross-correlation value was not significantly differentbetween groups.

The radius and percent determinism were not significantly differentamong the three groups. On the other hand, the percent recurrence wassignificantly greater in the young controls as compared with the olderage-matched controls (p=0.02). Entropy, maximum line length, and meanlength of lines was significantly increased in the patients with COPD ascompared with the older age-matched controls (p=0.02, p=0.009, andp=0.008, respectively).

One purpose of Experiment 2 was to investigate the effect of abnormalbreathing rhythms, not under voluntary control, on locomotor respiratorycoupling. The results indicate that patients with COPD demonstrate aless variable coupling and simpler frequency ratios as compared with thecontrols. Not only did patients with COPD demonstrate a 1:1 ratio thatwas not seen at all in their healthy counterparts, they alsodemonstrated higher temporal similarity (higher cross-correlation value)and increased strength of coupling (longer cRQA maximum lines), clearlydemonstrating a less flexible and adaptable behavior. Furthermore, theeffect of speed perturbations was investigated, as patients with COPDand their healthy counterparts were asked to walk at speeds slower andfaster than their self-selected walking paces. Although the four cRQAparameters (percent determinism, entropy, maximum line length, and themean length of lines) had a main effect of speed, of particular interestwere the interactions. A significant interaction was found in whichpatients with COPD demonstrated more patterns in line lengths at theself-selected and slowest speeds (cRQA entropy).

Overall, patients with COPD did demonstrate alterations in theircoupling as compared with age-matched controls, supporting thehypothesis that an involuntary, abnormal breathing rhythm can affectlocomotor respiratory coupling. Patients with COPD did in fact elicit amore rigid coupling pattern, as indicated by the increased length of themaximum line and a slightly increased temporal relationship as indicatedby the maximum cross-correlation. The cross-correlation valuesthemselves demonstrate no relationship in the age-matched controls;however, the patients with COPD demonstrate a weak relationship betweenthe two data sets. This indicates that although weak, there is more of arelationship in the patients with COPD as compared with the controls.Furthermore, based upon the Farey tree representing the hierarchy in thesine circle map, COPD patients demonstrate less flexibility due to theirdecreased range and simpler (less complex) coupling ratios. This is dueto the fact that their most common frequency ratios were between 1:1 and2:1; only during one speed did a patient with COPD use 3:1 as the mostcommon ratio. A ratio of 1:1 indicates that both walking and breathingcoupled together at the exact same frequency, and a ratio of 2:1indicates that two steps were taken for one breath. This type of simplepattern has been interpreted as a loss of ability to utilize higherorder ratios (e.g., 3:1, 3:2, 4:1) due to increases in task difficulty.

Moreover, when patients with COPD were compared with healthy youngadults and older age-matched controls while walking at theirself-selected speeds, clearer differences between the patients with COPDand their age-matched controls were uncovered. Here, it can be seen thatindeed, patients with COPD demonstrate increased patterns in linelengths, increased coupling strength, and coupled locomotor andrespiratory rhythms more often. These differences are seen between justthe age-matched controls and the patients with COPD (Table 8). Due tothe less conservative nature of this statistical test and that fewercomparisons are being tested, it is not surprising that thesedifferences are seen here. Nevertheless, this truly demonstrates that aninvoluntary, abnormal breathing rhythm does in fact impact how thelocomotor and respiratory systems couple.

As stated previously, there are at least two possible physiologicalmechanisms. One is due to chemoreception of effective oxygenconcentration in the lungs, and the other is due to disruptions inneural input and outputs. The design of this study did not allow forinvestigation into the driving mechanism behind the locomotorrespiratory coupling; however, there are a few inferences that can beconsidered. First, patients with COPD do suffer from dynamichyperinflation or, simply, air trapping; the pathophysiology of thedisease results in less elastic recoil of the lungs during expiration.Therefore, as these patients expire, less air leaves the lungs than whatentered the lungs previously. Eventually this leads to a smaller andsmaller tidal volume, increases in breathlessness, and worseningventilation perfusion matching, resulting in hypoxemia that worsens withexercise. A decrease in the effective oxygen concentration in the lungsmay with the ability of respiration to synchronize with movement.Second, patients with COPD suffer from abnormal body cell massalterations, muscular protein degradation leading to musclewasting/atrophy, impaired energy production and metabolic performance,and increased susceptibility to muscle fatigue and weakness.Furthermore, it appears that afferent sensory loss is present in thesepatients as well. Consequently, neural-mechanical mechanisms would alsobe disrupted in patients with COPD.

Alternatively, the mechanism could be unrelated to these physiologicalexplanations. Some dynamic systems theory is based upon behavioralattractors and how preferred (i.e., stable) that attractor is. Preferredperformance in certain situations can be along a continuum of very rigidto very random. This has been illustrated as the “depth of the well.” Ifyou have a small ball in a shallow well, small perturbations may causethe ball to move out of the well and into another. However, the oppositeis true when the well is very deep. Patients with COPD demonstrate a“deep well,” corresponding to a very rigid coupling pattern. They prefera 1:1 or 2:1 coupling that is much tighter than their healthycounterparts, and it is very difficult for them to phase-shift intoanother behavioral state. Rehabilitation programs, designed from adynamic systems theory perspective, may use control parameters to try tomove the behavioral attractor from one state to another or, as in theprevious illustration, from one well to another well. From thisperspective, in the current study, speed was applied as a controlparameter in an attempt to move subjects from one behavioral attractorto another. If the coupling is so tight that perturbations are not welltolerated, the use of perturbations such as speed is more detrimentalthan helpful. When the coupling is as tight as it was in the patientswith COPD, locomotion may bring about too large of a change in theirbreathing dynamics, making it too difficult to keep a steady rhythm.This causes them to lock into these 1:1 and 2:1 coupling patterns evenmore so. Therefore, smaller perturbations at their comfortable walkingspeed should be considered. For example, giving COPD patients a stimulusto walk with a different pattern could elicit a small enough change inwalking dynamics to cause a helpful change in breathing dynamics.

Further differences between the patients with COPD and their age-matchedcontrols can be seen when speed perturbations are introduced. Speedalone elicited changes in cRQA percent determinism, entropy, maximumline length, and mean length of lines, demonstrating that coupling is infact altered under different task demands. Interestingly, however, thespeed that demonstrated the most differences from all the other speedswas the slowest speed, −20%. At −20%, both groups demonstrated anincreased number of points being associated with a diagonal line,possibly indicating a more overall periodic movement pattern. Thepatterns in the lengths of lines were also significantly increased, aswell as the length of the longest line and the mean length of lines. Theslowest walking speed appears to have caused the patients with COPD tolock into this rigid pattern even more so.

In particular, patients with COPD demonstrated more patterns in linelengths during the self-selected speed and the −10% condition but hadsimilar patterns in line lengths during the +10%, −20%, and +20%conditions (FIG. 20). The representative recurrence plot of theage-matched control (FIG. 18) shows a very distinct pattern in which thepeaks of the breathing pattern relate to the valleys of the trochantermovement. This would indicate that breath inspiration was recurrent withthe moment right before toe-off. Compared with the representativerecurrence plot for the patient with COPD (FIG. 19), the patient withCOPD clearly demonstrates longer diagonal lines (more strength incoupling) and without the distinct pattern in which inspiration isclearly recurrent with one portion of the gait cycle. Although thepatterns in line lengths are different in the patients with COPD(entropy), the maximum line lengths were significantly longer. Takentogether with the rigidity of the frequency ratio, this can beeninterpreted that the coupling is tighter and this will happen more oftenthan in age-matched controls.

Experiment 2 has demonstrated that an involuntary, abnormal breathingrhythm does have an effect on locomotor respiratory coupling. Inaddition, speed perturbations can further affect one's ability to couplethese two systems. Rehabilitation protocols may focus on techniques thatcan “push” these patients out of their tight coupling and cause them toexplore other, less simple, frequency ratios. This may increase theirrepertoire of adaptable and flexible behavioral states.

Overall, the two experiments in this study investigated the effect ofvoluntary and involuntary abnormal breathing rhythms on the ability tocouple the locomotor and respiratory systems. In Experiment 1, younghealthy controls were asked to walk at a self-selected speed and breatheeither at their normal pace, a faster pace, or a slower pace. Eventhough stepping frequency was constrained to the speed of the treadmill,the healthy young subjects were able to still couple locomotion to therespiratory rhythm. The current results provide further evidence thatbidirectionality exists between the two systems and that this could bedue to a neural/central command driving mechanism. As brain stemcontrols to respiration have been clearly identified, it has been shownthat signals from the cervical and lumbar limb afferent inputs areconveyed to the brain stem respiratory centers. Further, themesencephalic locomotor region has been shown to have projections to therespiratory control centers and to locomotor control. In the currentstudy, by voluntarily altering breathing rhythm, the respiratory braincenter was able to coordinate this information with the locomotorcenters allowing these subjects to still couple under abnormal breathingconditions without any problems.

In addition, Experiment 1 also investigated the effect of mismatchingfrequencies on coupling. When subjects walked slower and were instructedto breathe slowly, healthy young subjects demonstrated significantlymore patterns in line lengths and stronger coupling between the tworhythms, and the average length of time that the two data sets werecoupled was longer as compared with when they were asked to walk slowerbut breathe faster. Subjectively, subjects commented that thismismatched condition was difficult to do and required a lot ofconcentration. It is possible that instructing subjects to breatheslowly while having them walk faster than their self-selected walkingpaces was the hardest condition presented to them. Walking slowly maynot be a task that these individuals are presented with often, andfuture studies may want to consider a gradient of speeds to see if thereis a speed that elicits a breakpoint in locomotor respiratory coupling.

In Experiment 2, patients with COPD and age-matched controls were testedto investigate the effect of involuntary, abnormal breathing patterns onlocomotor respiratory coupling. Patients with COPD demonstrated a strictrange of coupling ratios that represented a stabler and simplercoupling, as well as stronger temporal similarity and increased couplingstrength. Moreover, when patients with COPD were compared with healthyyoung adults and older age-matched controls at their self-selectedspeeds, patients with COPD demonstrated increased patterns in linelengths, a greater coupling strength and couple locomotor, andrespiratory rhythms more often as compared with their age-matchedcontrols; hence, a very tight and rigid coupling pattern. Involuntary,abnormal breathing rhythm does in fact impact the ability of thelocomotor and respiratory systems to couple. It causes coupling tobecome very simple, 1:1 as opposed to 3:1 or 4:1, and causes a veryrigid behavioral state. From a dynamic systems theory perspective, thispreferred performance is very rigid, and rehabilitation programs may bedesigned to use control parameters to try to move the behavioralattractor from one state to another.

Moreover, in Experiment 2, speed perturbations were introduced and theresults confirmed that coupling is in fact altered under alteredworkloads (e.g., speed). It is interesting to note that the speedchanges were elicited mainly at the slowest walking speed. It appearsthat walking at the slowest speed presented the tightest coupling of allspeeds. Furthermore, a significant interaction for entropy was foundwhen patients with COPD demonstrated more patterns in line lengthsduring differing speeds than their age-matched controls. Demonstratingthat at the slower speeds, they were able to increase their ability tocouple during different patterns or increase the number of patterns thatallowed for entrainment.

Reporting of the most common frequency ratio did not demonstrate anysignificant changes between any of the groups, conditions, or speeds.The most common frequency ratio was almost always two heel strikes toone breath. This ratio is consistent with published literature. Althoughthe ranges of commonly utilized frequency ratios are reported, thisprovides very little information about the strength of the couplingand/or how often the coupling is happening. Cross-correlation was alsotested, as it has been used previously in locomotor respiratorycoupling. However, cross-correlation was not a strong indicator ofcoupling in the current study. For most conditions, groups, or speeds,the cross-correlation was found to have a weak or no relationship.Although significant, these weak relationships do not add furtherinformation to the findings. As cross-correlation is a linearmathematical tool, it may not be able to capture the dynamics of twosystems clearly. In addition, cross-correlation looks for the maximumcorrelation at one time-delayed point. This provides little to noinformation regarding coupling over time, and/or the strength ofcoupling over multiple evolutions of the two systems.

The cRQA was the third tool that was developed to quantify coupling inthe present study. As cRQA is derived from the analysis of recurrenceplots, it inherently is designed to quantify coupling and strength ofcoupling over time. This allows the user to quantify subtle nonlinearinteractions. In the current study, strength of coupling over time, theaverage lengths of time that locomotion and respiration was coupled andthe different complexities of the coupling were revealed. Based upon thefindings within these two experiments, it appears that cRQA is aneffective tool that can be used to describe locomotor respiratorycoupling.

TABLE 1 Subject demographics for all subjects in both Experiments 1 and2. Mean (standard deviation) are reported. Young Controls Older ControlsPatients w/COPD N = 12 N = 14 N = 6 F_(2.29) p Gender Males = 12 Males =12 Males = 6 Age (years) 27.33 (5.48) 66.00 (11.40) * 63.67 (10.21) #61.85 <0.001 Height (cm) 183.82 (7.03) 177.12 (8.52) 180.36 (8.74) 2.250.12 Weight (kg) 88.10 (13.59) 80.91 (15.01) 109.70 (40.13) {circumflexover ( )} 3.90 0.03 Preferred 1.33 (0.20) 1.17 (0.31) 0.83 (0.14) #{circumflex over ( )} 8.24 0.001 gait speed (m/s) (Note: * indicatessignificant difference between young and older controls; # indicatessignificant difference between young controls and patients with COPD;{circumflex over ( )} indicates significant difference between oldercontrols and patients with COPD).

TABLE 2 Comparison of mean(standard deviation) frequency ratios betweeneach of the seven walking conditions. (Note: The following symbolsrepresent significant difference from (‡) SELF, (#) SLOW SELF, (@) FASTSELF, ({circumflex over ( )}) SLOW +20%, (†) FAST +20%, and (£) SLOW−20%.) NORM SLOW FAST FAST SLOW FAST Dependent Variable SELF SELF SELFSLOW +20% +20% −20% −20% F_(6,54) p Range of most common 2:1-5:1 2:1-9:12:1-7:1 2:1-6:1 2:1-5:1 2:1-8:1 2:1-4:1 frequency ratios Most commonfrequency 2:1/ 3:1/ 2:1/ 3:1/ 2:1/ 2:1/ 2:1/ ratio/percent occurrence46.1% 25.7% 32.9% 32.0% 34.3% 31.7% 49.2% Most common number of 2.6 3.93.0 4.1  3.0  3.2  2.3 2.9 0.02 heel strikes in one breath (0.52) (2.23)(0.94) (1.37)‡  (1.05)  (1.23)  (0.67){circumflex over ( )} Total numberof breaths 17.4 11.5 14.3 11.5 15.3 12.1 14.0 3.3 0.01 (3.24) (4.20)‡(5.70) (3.31)‡@  (5.40)  (4.20)‡  (2.71)‡#{circumflex over ( )} Totalnumber of heel strikes 45.0 41.3 44.3 44.3 48.9 36.6 40.2 10.1 <0.001(3.83) (6.68)‡ (5.36) (6.11)  (3.25)‡#@{circumflex over ( )} (7.15)‡#@{circumflex over ( )}†  (3.68)‡@{circumflex over ( )}†

TABLE 3 Comparison of mean(standard deviation) cross-correlation (XC)variables between each of the seven walking conditions. (Note: (*)indicates that the variable did not met the terms of normality and theGreenhouse-Geisser correction is reported. The following symbolsrepresent significant difference from (‡) SELF, (#) SLOW SELF, (@) FASTSELF, ({circumflex over ( )}) SLOW +20%, (†) FAST +20%, and (£) SLOW−20%.) NORM SLOW SLOW FAST SLOW FAST Dependent Variable SELF SELF FASTSELF +20% +20% −20% −20% F_(6,54) p Max XC* 0.184 0.161 0.158 0.1070.113 0.160 (0.075)  0.259 7.0 0.004 (0.074) (0.043) (0.067) (0.036)‡#@(0.037)‡#@ (0.107)#@{circumflex over ( )}†£

TABLE 4 Comparison of mean(standard deviation) cRQA variables betweeneach of the seven walking conditions for the breath and trochanter datasets. (Note: (*) indicates that the variable did not met the terms ofnormality and the Greenhouse-Geisser correction is reported. Thefollowing symbols represent significant difference from (‡) SELF, (#)SLOW SELF, (@) FAST SELF, ({circumflex over ( )}) SLOW +20%, (†) FAST+20%, and (£) SLOW −20%.) Dependent NORM SLOW FAST FAST SLOW FASTVariable SELF SELF SELF SLOW +20% +20% −20% −20% F_(6,54) p Radius* 14.814.7 15.5 14.9 15.6  14.2  16.6 0.9 0.43 (3.1) (3.8) (2.6) (3.7) (3.8)  (3.5)   (3.6) % Recurrence 2.5 2.4 2.3 2.3 2.4  2.4  2.4 1.0 0.43(0.35) (0.18) (0.27) (0.30) (0.22)   (0.35)   (0.20) % Determinism 87.588.3 85.2 86.1 82.9  91.3  88.7 2.8 0.02 (5.2) (5.3) (6.2) (7.5) (9.5)  (3.6)#@†   (5.1)@£ Entropy 4.5 4.3 4.4 4.0 4.2  4.5  4.6 2.6 0.03(0.35) (0.44) (0.45) (0.55)‡@ (0.62)   (0.64){circumflex over ( )}  (0.52){circumflex over ( )} Max Line 87.2 85.4 84.9 69.3 79.3 124.1157.7 5.9 <0.001 (28.3) (39.7) (40.5) (24.6) (36.5)  (58.6){circumflexover ( )}  (55.9)‡#@{circumflex over ( )} Mean Line 14.6 14.3 14.4 12.713.1  15.8  16.2 2.7 0.02 (2.1) (2.8) (2.9) (3.5) (3.0)  (4.3){circumflex over ( )}   (3.7){circumflex over ( )}

TABLE 5 Comparison of mean(standard deviation) frequency ratios betweenpatients with COPD and the age-matched controls for each of the fivewalking speeds. (Note: (*) indicates that the variable did not met theterms of normality and the Greenhouse-Geisser correction is reported.The following symbols represent significant difference from (‡) SELF,(#) −10% (@) +10% and ({circumflex over ( )}) −20%.) Group SpeedInteraction Dependent Variable SELF −10% +10% −20% +20% F_(1,14) pF_(4,56) p F_(4,56) p Range of most common Control 2:1-3:1 2:1-6:12:1-3:1 2:1-3:1 2:1-3:1 frequency ratios COPD 1:1-2:1 1:1-2:1 2:11:1-3:1 2:1 Most common frequency Control 2:1/59.9% 2:1/56.6% 2:1/50.6%2:1/66.9% 2:1/46.8% ratio/percent occurrence COPD 2:1/56.7% 2:1/59.4%2:1/56.5% 2:1/46.2% 2:1/62.4% Most common number of Control  2.1(0.30) 2.2(0.40)  2.2(0.40)  2.0(0.00)  2.3(0.47) 4.0 0.07 1.7 0.20 0.27 0.81heel strikes in one COPD  1.8(0.45)  1.8(0.45)  2.0(0.00)  1.8(0.45) 2.0(0.00) breath* Total number of breaths Control 18.2(3.8) 17.8(3.5)18.6(3.7) 18.2(3.5) 18.5(4.4) 0.001 0.97 0.56 0.69 0.58 0.68 COPD18.4(3.2) 17.4(3.8) 17.0(3.3) 19.2(4.1) 19.0(2.2) Total number of heelControl 43.3(4.6) 42.3(4.4) 45.6(4.8)# 38.7(5.2)‡#@47.6(5.9)‡#@{circumflex over ( )} 5.7 0.03 25.9 <0.001 1.2 0.32 strikesCOPD 38.8(4.3) 36.2(3.3) 39.0(4.8) 34.6(3.4) 40.8(4.5)

TABLE 6 Comparison of mean(standard deviation) cross-correlation (XC)between patients with COPD and the age-matched controls for each of thefive walking speeds. (Note: (*) indicates that the variable did not metthe terms of normality and the Greenhouse-Geisser correction isreported. The following symbols represent significant difference from(‡) SELF, (#) −10%, (@) +10%, and ({circumflex over ( )}) −20%.)Dependent Group Speed Interaction Variable SELF −10% +10% −20% +20%F_(1,15) p F_(4,60) p F_(4,60) p Max XC* Control 0.18(0.06) 0.19(0.11)0.15(0.05) 0.21(0.11) 0.12(0.06) 5.9 0.03 0.22 0.85 3.1 0.05 COPD0.27(0.12) 0.24(0.10) 0.26(0.12) 0.24(0.10) 0.31(0.17)

TABLE 7 Comparison of mean(standard deviation) cRQA variables betweenpatients with COPD and the age-matched controls for each of the fivewalking speeds for the breath and the trochanter data sets. (Note: (*)indicates that the variable did not met the terms of normality and theGreenhouse-Geisser correction is reported. The following symbolsrepresent significant difference from (‡) SELF, (#) −10%, (@) +10%, and({circumflex over ( )}) −20%.) Group Speed Interaction SELF −10% +10%−20% +20% F_(1,15) p F_(4,60) p F_(4,60) p Radius Control 15.9(4.3)15.6(3.6) 17.1(5.4)  16.6(4.8)  17.6(4.6) 0.28 0.61 0.82 0.52 0.37 0.83COPD 15.3(2.8) 15.3(2.1) 17.2(2.8)  15.5(3.5)  15.3(2.1) % Re- Control 2.2(0.15)  2.4(0.28)  2.4(0.24)  2.5(0.29)  2.4(0.35) 0.20 0.89 0.840.51 1.0 0.40 currence COPD  2.3(0.24)  2.4(0.29)  2.4(0.19)  2.2(0.10) 2.5(0.19) % Deter- Control  79.4(13.0)  80.8(13.2) 81.6(10.1) 89.8(5.8)‡#@  76.4(14.0){circumflex over ( )} 4.4 0.05 4.6 0.003 1.50.21 minism COPD 91.6(4.9) 91.9(3.8) 85.9(6.5)  93.3(3.8)  87.5(6.3)Entropy* Control  3.7(1.0)  3.7(1.1)  3.8(0.71)‡  4.7(0.68)#@ 3.5(0.89){circumflex over ( )} 2.8 0.11 6.5 0.002 3.2 0.04 COPD 4.8(0.67)  4.7(0.53)  3.9(0.80)  4.8(0.70)  4.2(0.89) Max Line Control 74.6(39.7)  78.3(35.8) 65.7(33.6)‡# 122.3(71.5)@  58.2(22.8){circumflexover ( )} 6.5 0.02 4.1 0.01 1.7 0.17 COPD 129.2(44.0) 146.0(56.3)91.3(38.7) 127.0(56.6) 106.8(54.8) Mean Control 11.7(3.7) 12.2(4.6)11.8(3.2)‡#  17.7(8.1)@  10.9(3.7){circumflex over ( )} 2.3 0.15 5.40.01 2.1 0.15 Line* COPD 17.9(5.4) 16.2(3.9) 12.3(2.9)  17.4(5.0) 14.1(4.8)

TABLE 8 Comparison of mean dependent variables between patients withCOPD, older age-matched controls and healthy young controls at theirself-selected speed. Dependent Variable F_(2.27) p Most common number ofheel strikes in one breath 3.3 0.05 Maximum cross-correlation value 3.40.05 cRQA radius 0.8 0.47 cRQA percent recurrence 4.4   0.02 * cRQApercent determinism 3.2 0.05 cRQA entropy 4.6  0.02 {circumflex over( )} cRQA maximum line length 5.3  0.01 {circumflex over ( )} cRQA meanline length 5.5  0.01 {circumflex over ( )} (Note: * indicatessignificant difference between young and older age-matched controls; #indicates significant difference between young controls and patientswith COPD; {circumflex over ( )} indicates significant differencebetween age-matched controls and patients with COPD).

FIG. 21 is a block diagram of an example computing device 2100 suitablefor use in practicing various ones of the disclosed embodiments. Asshown, the computing device 2100 may include one or more processors 2102(e.g., one or more processor cores) and a system memory 2104. For thepurpose of this application, including the claims, the terms “processor”and “processor cores” may be considered synonymous, unless the contextclearly requires otherwise. As used herein, the term “processor” or“processing device” may refer to any device or portion of a device thatprocesses electronic data from registers and/or memory to transform thatelectronic data into other electronic data that may be stored inregisters and/or memory. The processor(s) 2102 may include one or moremicroprocessors, graphics processors, digital signal processors, cryptoprocessors, or other suitable devices.

The computing device 2100 may include one or more mass storage devices2106 (such as diskettes, hard drives, solid-state drives, CD-ROMs, flashmemory devices, and so forth). The mass storage device 2106 and/or thesystem memory 2104 may include any of the embodiments, or portions ofthe embodiments, of the storage device 206 discussed above withreference to FIG. 2. The system memory 2104 and the mass storage device2106 may include any suitable storage devices, such as volatile memory(e.g., dynamic random access memory (DRAM)), nonvolatile memory (e.g.,read-only memory (ROM), and flash memory. The computing system 2100 mayinclude one or more I/O devices 2108 (such as display, keyboard, cursorcontrol, network interface cards, modems, and so forth). The I/O devices2108 may include any of the I/O devices 202 discussed above withreference to FIG. 2. The elements may be coupled to each other via asystem bus 2112, which represents one or more buses. In the case ofmultiple buses, they may be bridged by one or more bus bridges (notshown).

Each of these elements may perform its conventional functions known inthe art. In particular, the system memory 2104 and the mass storagedevice 2106 may be employed to store a working copy and a permanent copyof programming instructions implementing any of the methods disclosedherein (e.g., the method of FIG. 11), or portions thereof, hereincollectively denoted as instructions 2122. Various methods and systemcomponents may be implemented by assembler instructions supported byprocessor(s) 2102 or high-level languages, such as, for example, C, thatcan be compiled into such instructions. For example, the computingdevice 2100 configured with suitable instructions 2122 may provide anysuitable ones of the logic components disclosed herein.

The permanent copy of the programming instructions may be placed intopermanent mass storage devices 2106 in the factory, or in the field,through, for example, a machine-accessible distribution medium (notshown), such as a compact disc (CD), or through a communication deviceincluded in the I/O devices 2108 (e.g., from a distribution server (notshown)). That is, one or more distribution media having animplementation of the agent program may be employed to distribute theagent and program various computing devices. The constitution ofelements 2102-2112 are known, and accordingly will not be furtherdescribed.

Machine-accessible media (including non-transitory computer-readablestorage media), methods, systems, and devices for performing theabove-described techniques are illustrative examples of embodimentsdisclosed herein. For example, a computer readable media (e.g., thesystem memory 2104 and/or the mass storage device 2106) may have storedthereon instructions (e.g., the instructions 2122) such that, when theinstructions are executed by one or more processors 2102, theinstructions cause the computing device 2100 to perform any of themethods disclosed herein.

In various implementations, the computing device 2100 may be a laptop, anetbook, a notebook, an ultrabook, a smartphone, a tablet, a personaldigital assistant (PDA), an ultra mobile PC, a mobile phone, a desktopcomputer, a server, a printer, a scanner, a monitor, a set-top box, anentertainment control unit, a digital camera, a portable music player,or a digital video recorder. In some implementations, the computingdevice 2100 may be any other electronic device that processes data. Insome embodiments, the COPD severity estimation systems and techniquesmay be implemented, at least in part, in a high-performance computingdevice. In some embodiments, the COPD severity estimation systems andtechniques described herein may be implemented, at least in part, inhandheld computing devices.

The following paragraphs provide examples of various ones of theembodiments disclosed herein.

Example 1 is a system for estimating severity of chronic obstructivepulmonary disease (COPD) in a patient, including: a first logic toreceive a breathing signal representative of breathing activity of thepatient over a time interval; a second logic to receive a locomotionsignal representative of locomotive activity of the patient over thetime interval; a third logic to provide breathing data and locomotiondata to a fourth logic, wherein: the fourth logic is to generate anestimate of COPD severity in the patient by comparison of 1) across-recurrence quantification analysis (cRQA) parameter between thebreathing data and the locomotion data and 2) a reference value, thebreathing data is based on the breathing signal, and the locomotion datais based on the locomotion signal.

Example 2 may include the subject matter of Example 1, and may furtherspecify that the first logic is to receive the breathing signal from achest strap sensor coupled to the first logic.

Example 3 may include the subject matter of Example 2, and may furtherspecify that the chest strap sensor is a resistive sensor.

Example 4 may include the subject matter of any of Examples 2-3, and mayfurther specify that the first logic is included in a computing device,and a connector of the chest strap sensor is removably couplable with aconnector of the computing device.

Example 5 may include the subject matter of Example 4, and may furtherspecify that the computing device has a housing, and the housing issized to be received in a pocket of the chest strap sensor.

Example 6 may include the subject matter of any of Examples 1-5, and mayfurther specify that the second logic is included in a computing devicehaving a housing, the second logic is to receive the locomotion signalfrom an accelerometer coupled to the second logic, and the accelerometeris included in the housing.

Example 7 may include the subject matter of Example 6, and may furtherspecify that the first logic is to receive the breathing signal from achest strap sensor coupled to the first logic, the first logic isincluded in the computing device having the housing, and the housing issized to be received in a pocket of the chest strap sensor.

Example 8 may include the subject matter of any of Examples 1-7, and mayfurther specify that the first logic, the second logic, and the thirdlogic are included in a common housing.

Example 9 may include the subject matter of any of Examples 1-8, and mayfurther include the fourth logic.

Example 10 may include the subject matter of Example 9, and may furtherspecify that the first logic, the second logic, the third logic, and thefourth logic are included in a common housing.

Example 11 may include the subject matter of any of Examples 1-10, andmay further specify that the fourth logic is included in a housing of acomputing device remote from the third logic.

Example 12 may include the subject matter of Example 11, and may furtherspecify that the third logic is to provide the breathing data and thelocomotion data to the fourth logic by providing the breathing data andthe locomotion data for storage in a storage device that is accessibleto the fourth logic.

Example 13 may include the subject matter of Example 12, and may furtherspecify that the storage device is remote from the third logic and fromthe fourth logic.

Example 14 may include the subject matter of any of Examples 1-13, andmay further specify that the first logic and the second logic areincluded in a first housing, and the third logic is included in a secondhousing different from the first housing.

Example 15 may include the subject matter of Example 14, and may furtherspecify that the second housing is a housing of a dock computing devicehaving at least one connector to receive a mating connector of the firsthousing.

Example 16 may include the subject matter of Example 15, and may furtherspecify that the first housing includes a storage device coupled to thefirst logic and the second logic, the first logic is to store thebreathing data or the breathing signal in the storage device, the secondlogic is to store the locomotion data or the locomotion signal in thestorage device, and the third logic is to read information stored in thestorage device when the housing is mated with the dock computing devicevia the connectors.

Example 17 may include the subject matter of any of Examples 15-16, andmay further specify that the dock computing device includes a fifthlogic to charge a power supply included in the first housing and coupledto the first and second logic.

Example 18 may include the subject matter of any of Examples 14-17, andmay further specify that the first housing includes a storage devicecoupled to the first logic and the second logic, the first logic is tostore the breathing data or the breathing signal in the storage device,the second logic is to store the locomotion data or the locomotionsignal in the storage device, and a wireless communication device isincluded in the first housing to provide information stored in thestorage device for access by the third logic.

Example 19 may include the subject matter of any of Examples 14-17, andmay further specify that the third logic is included in a smartphone.

Example 20 may include the subject matter of any of Examples 14-19, andmay further specify that the second housing includes a wirelesscommunication device to provide the breathing data and the locomotiondata wirelessly from the third logic to the fourth logic.

Example 21 may include the subject matter of any of Examples 1-20, andmay further specify that the system is to low-pass filter the breathingsignal with a cutoff frequency of 0.5 Hz to generate the breathing data.

Example 22 may include the subject matter of Example 21, and may furtherspecify that the first logic is to generate the breathing data byfiltration of the breathing signal.

Example 23 may include the subject matter of any of Examples 21-22, andmay further specify that the third logic is to generate the breathingdata by filtration of the breathing signal.

Example 24 may include the subject matter of any of Examples 1-23, andmay further specify that the breathing data is the breathing signal.

Example 25 may include the subject matter of any of Examples 1-24, andmay further specify that the system is to low-pass filter the locomotionsignal with a cutoff frequency of 10 Hz to generate the locomotion data.

Example 26 may include the subject matter of Example 25, and may furtherspecify that the first logic is to generate the locomotion data byfiltration of the locomotion signal.

Example 27 may include the subject matter of any of Examples 25-26, andmay further specify that the third logic is to generate the locomotiondata by filtration of the breathing signal.

Example 28 may include the subject matter of any of Examples 1-27, andmay further specify that the locomotion data is the locomotion signal.

Example 29 may include the subject matter of any of Examples 1-28, andmay further specify that the fourth logic is to provide the estimate ofCOPD severity to a fifth logic, and the fifth logic is to cause theestimate of COPD severity in the patient to be displayed on a displaydevice.

Example 30 may include the subject matter of Example 29, and may furtherspecify that the fifth logic is to cause the estimate of COPD severityin the patient to be displayed on the display device simultaneously witha BODE index for the patient, a GOLD classification for the patient, oran EXACT score for the patient.

Example 31 may include the subject matter of any of Examples 29-30, andmay further specify that the fifth logic is to cause the estimate ofCOPD severity in the patient to be displayed on the display devicesimultaneously with a GOLD classification for the patient.

Example 32 may include the subject matter of any of Examples 29-31, andmay further specify that the fifth logic is to cause the estimate ofCOPD severity in the patient to be displayed on the display devicesimultaneously with an EXACT score for the patient.

Example 33 may include the subject matter of any of Examples 1-32, andmay further include: a fifth logic to receive a heart rate signalrepresentative of a rate of heartbeats of the patient over the timeinterval; wherein the third logic is to provide heart rate data, basedon the heart rate signal, to a sixth logic, and the sixth logic is tocause the estimate of COPD severity in the patient to be displayed on adisplay device simultaneously with a heart rate of the patient based onthe heart rate data.

Example 34 may include the subject matter of any of Examples 1-33, andmay further specify that the time interval is at least 45 seconds.

Example 35 may include the subject matter of any of Examples 1-34, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a percent determinism.

Example 36 may include the subject matter of Example 35, and may furtherspecify that an increased percent determinism is associated with anestimate of greater severity of COPD.

Example 37 may include the subject matter of any of Examples 1-36, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a mean diagonal line length.

Example 38 may include the subject matter of Example 37, and may furtherspecify that a diagonal line used in a determination of mean diagonalline length represents at least 100 milliseconds of breathing data andlocomotion data.

Example 39 may include the subject matter of any of Examples 37-38, andmay further specify that an increased mean diagonal line length isassociated with an estimate of greater severity of COPD.

Example 40 may include the subject matter of any of Examples 1-39, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes an entropy.

Example 41 may include the subject matter of Example 40, and may furtherspecify that an increased entropy is associated with an estimate ofgreater severity of COPD.

Example 42 may include the subject matter of any of Examples 1-41, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a percent recurrence.

Example 43 may include the subject matter of Example 42, and may furtherspecify that a decreased percent recurrence is associated with anestimate of greater severity of COPD.

Example 44 may include the subject matter of any of Examples 1-43, andmay further specify that the fourth logic is to identify a predeterminedembedding dimension and to utilize the embedding dimension in adetermination of the cRQA parameter.

Example 45 may include the subject matter of any of Examples 1-44, andmay further specify that the fourth logic is to identify a predeterminedtime delay, and to utilize the time delay in a determination of the cRQAparameter.

Example 46 may include the subject matter of any of Examples 1-45, andmay further include a fifth logic to generate a prompt to the patient tobegin locomotion.

Example 47 may include the subject matter of any of Examples 1-46, andmay further specify that a receipt of the locomotion signal by thesecond logic is initiated by detection of locomotive activity of thepatient.

Example 48 may include the subject matter of any of Examples 1-47, andmay further specify that the fourth logic is to provide the estimate ofCOPD severity in the patient to a fifth logic, and the fifth logic is toprovide the estimate of COPD severity in the patient to a remotecomputing device associated with a care provider.

Example 49 may include the subject matter of any of Examples 1-48, andmay further specify that the breathing signal is a discrete-time signalhaving a first sampling rate, the locomotion signal is a discrete-timesignal having a second sampling rate different from the first samplingrate, the third logic is to interpolate and resample the breathingsignal to the second sampling rate when the first sampling rate is lessthan the second sampling rate, and the third logic is to interpolate andresample the locomotion signal to the first sampling rate when thesecond sampling rate is less than the first sampling rate.

Example 50 may include the subject matter of any of Examples 1-49, andmay further specify that the reference value is a value of the cRQAparameter from a reference population.

Example 51 may include the subject matter of Example 50, and may furtherspecify that the reference population is a population without COPD.

Example 52 may include the subject matter of any of Examples 1-51, andmay further specify that the reference value is a previously obtainedvalue of the cRQA parameter from the patient.

Example 53 may include the subject matter of any of Examples 1-52, andmay further specify that the estimate of COPD severity in the patient isan indication that the patient's COPD has increased in severity from aprevious time.

Example 54 may include the subject matter of any of Examples 1-53, andmay further specify that the estimate of COPD severity in the patient isan indication that the patient's COPD is elevated with respect to areference population.

Example 55 is a method for estimating severity of chronic obstructivepulmonary disease (COPD) in a patient, including: receiving, by acomputing device, breathing data and locomotion data to a fourth logic,wherein the breathing data is representative of breathing activity ofthe patient over a time interval, and the locomotion data isrepresentative of locomotive activity of the patient over the timeinterval; generating, by the computing device, an estimate of COPDseverity in the patient by comparing 1) a cross-recurrencequantification analysis (cRQA) parameter between the breathing data andthe locomotion data and 2) a reference value; and providing, by thecomputing device, the estimate of COPD severity for display.

Example 56 may include the subject matter of Example 55, and may furtherspecify that the breathing data is generated via use of a chest strapsensor.

Example 57 may include the subject matter of Example 56, and may furtherspecify that the chest strap sensor is a resistive sensor.

Example 58 may include the subject matter of any of Examples 55-57, andmay further specify that the locomotion data is generated via use of anaccelerometer.

Example 59 may include the subject matter of any of Examples 55-58, andmay further specify that the computing device accesses the breathingdata and the locomotion data from a storage device remote from thecomputing device.

Example 60 may include the subject matter of Example 59, wherein thecomputing device is a first computing device, and the breathing data andthe locomotion data are stored in the storage device by a secondcomputing device different from the first computing device.

Example 61 may include the subject matter of Example 60, and may furtherspecify that the second computing device is a dock computing device toreceive a wearable computing device including a breathing sensor and alocomotion sensor.

Example 62 may include the subject matter of any of Examples 55-61, andmay further include low-pass filtering, by the computing device, thebreathing data with a cutoff frequency of 0.5 Hz.

Example 63 may include the subject matter of any of Examples 55-62, andmay further include low-pass filtering, by the computing device, thelocomotion data with a cutoff frequency of 10 Hz.

Example 64 may include the subject matter of any of Examples 55-63, andmay further specify that the display of the estimate of COPD severity inthe patient includes a BODE index for the patient, a GOLD classificationfor the patient, or an EXACT score for the patient.

Example 65 may include the subject matter of any of Examples 55-64, andmay further specify that the display of the estimate of COPD severity inthe patient includes a GOLD classification for the patient.

Example 66 may include the subject matter of any of Examples 55-65, andmay further specify that the display of the estimate of COPD severity inthe patient includes an EXACT score for the patient.

Example 67 may include the subject matter of any of Examples 55-66, andmay further specify that the display of the estimate of COPD severity inthe patient includes a heart rate of the patient.

Example 68 may include the subject matter of any of Examples 55-67, andmay further specify that the time interval is at least 45 seconds.

Example 69 may include the subject matter of any of Examples 55-68, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a percent determinism.

Example 70 may include the subject matter of Example 69, and may furtherspecify that an increased percent determinism is associated with anestimate of greater severity of COPD.

Example 71 may include the subject matter of any of Examples 55-70, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a mean diagonal line length.

Example 72 may include the subject matter of Example 71, and may furtherspecify that a diagonal line used in the determination of the meandiagonal line length represents at least 100 milliseconds of breathingdata and locomotion data.

Example 73 may include the subject matter of any of Examples 71-72, andmay further specify that an increased mean diagonal line length isassociated with an estimate of greater severity of COPD.

Example 74 may include the subject matter of any of Examples 55-73, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes an entropy.

Example 75 may include the subject matter of Example 74, and may furtherspecify that an increased entropy is associated with an estimate ofgreater severity of COPD.

Example 76 may include the subject matter of any of Examples 55-75, andmay further specify that the cRQA parameter between the breathing dataand the locomotion data includes a percent recurrence.

Example 77 may include the subject matter of Example 76, and may furtherspecify that a decreased percent recurrence is associated with anestimate of greater severity of COPD.

Example 78 may include the subject matter of any of Examples 55-77, andmay further include identifying, by the computing device, apredetermined embedding dimension, and utilizing, by the computingdevice, the embedding dimension in the determination of the cRQAparameter.

Example 79 may include the subject matter of any of Examples 55-78, andmay further include identifying, by the computing device, apredetermined time delay, and utilizing, by the computing device, thetime delay in the determination of the cRQA parameter.

Example 80 may include the subject matter of any of Examples 55-79, andmay further specify that the reference value is a value of the cRQAparameter from a reference population.

Example 81 may include the subject matter of Example 80, and may furtherspecify that the reference population is a population age-matched to thepatient.

Example 82 may include the subject matter of any of Examples 55-81, andmay further specify that the reference value is a previously obtainedvalue of the cRQA parameter from the patient.

Example 83 may include the subject matter of any of Examples 55-82, andmay further specify that the estimate of COPD severity in the patient isan indication that the patient's COPD has increased in severity from aprevious time.

Example 84 may include the subject matter of any of Examples 55-83, andmay further specify that the estimate of COPD severity in the patient isan indication that the patient's COPD is elevated with respect to areference population.

Example 85 may include the subject matter of any of Examples 55-84, andmay further specify that the display of the estimate of COPD severity inthe patient includes an indicator of an activity level of the patient.

Example 86 may include the subject matter of any of Examples 1-54, andmay further specify that the display of the estimate of COPD severity inthe patient includes an indicator of an activity level of the patient.

Example 85 is one or more computer readable media having instructionsthereon that, in response to execution by one or more processing devicesof a computing device, cause the computing device to perform the methodof any of Examples 55-85.

Example 86 is a computing device comprising means for performing themethod of any of Examples 55-85.

1. A system for estimating severity of chronic obstructive pulmonarydisease (COPD) in a patient, comprising: a first logic to receive abreathing signal representative of breathing activity of the patientover a time interval; a second logic to receive a locomotion signalrepresentative of locomotive activity of the patient over the timeinterval; a third logic to provide breathing data and locomotion data toa fourth logic, wherein: the fourth logic is to generate an estimate ofCOPD severity in the patient by comparison of 1) a cross-recurrencequantification analysis (cRQA) parameter between the breathing data andthe locomotion data and 2) a reference value, the breathing data isbased on the breathing signal, and the locomotion data is based on thelocomotion signal.
 2. The system of claim 1, wherein the first logic isto receive the breathing signal from a chest strap sensor coupled to thefirst logic.
 3. The system of claim 2, wherein the first logic isincluded in a computing device, and a connector of the chest strapsensor is removably couplable with a connector of the computing device.4. The system of claim 3, wherein the computing device has a housing,and the housing is sized to be received in a pocket of the chest strapsensor.
 5. The system of claim 1, wherein the fourth logic is includedin a housing of a computing device remote from the third logic.
 6. Thesystem of claim 5, wherein the third logic is to provide the breathingdata and the locomotion data to the fourth logic by providing thebreathing data and the locomotion data for storage in a storage devicethat is accessible to the fourth logic.
 7. The system of claim 6,wherein the storage device is remote from the third logic and from thefourth logic.
 8. The system of claim 1, wherein the first logic and thesecond logic are included in a first housing, and the third logic isincluded in a second housing different from the first housing.
 9. Thesystem of claim 8, wherein the second housing is a housing of a dockcomputing device having at least one connector to receive a matingconnector of the first housing.
 10. The system of claim 9, wherein thefirst housing includes a storage device coupled to the first logic andthe second logic, the first logic is to store the breathing data or thebreathing signal in the storage device, the second logic is to store thelocomotion data or the locomotion signal in the storage device, and thethird logic is to read information stored in the storage device when thehousing is mated with the dock computing device via the connectors. 11.The system of claim 8, wherein the first housing includes a storagedevice coupled to the first logic and the second logic, the first logicis to store the breathing data or the breathing signal in the storagedevice, the second logic is to store the locomotion data or thelocomotion signal in the storage device, and a wireless communicationdevice is included in the first housing to provide information stored inthe storage device for access by the third logic.
 12. The system ofclaim 1, wherein the fourth logic is to provide the estimate of COPDseverity to a fifth logic, and the fifth logic is to cause the estimateof COPD severity in the patient to be displayed on a display device. 13.The system of claim 12, wherein the fifth logic is to cause the estimateof COPD severity in the patient to be displayed on the display devicesimultaneously with a BODE index for the patient, a GOLD classificationfor the patient, or an EXACT score for the patient.
 14. One or morenon-transitory computer readable media having instructions thereon that,in response to execution by one or more processing devices of acomputing device, cause the computing device to: receive breathing dataand locomotion data to a fourth logic, wherein the breathing data isrepresentative of breathing activity of the patient over a timeinterval, and the locomotion data is representative of locomotiveactivity of the patient over the time interval; generate an estimate ofCOPD severity in the patient by comparing 1) a cross-recurrencequantification analysis (cRQA) parameter between the breathing data andthe locomotion data and 2) a reference value; and provide the estimateof COPD severity for display.
 15. The one or more non-transitorycomputer readable media of claim 14, wherein the cRQA parameter betweenthe breathing data and the locomotion data includes a percentdeterminism.
 16. The one or more non-transitory computer readable mediaof claim 15, wherein an increased percent determinism is associated withan estimate of greater severity of COPD.
 17. The one or morenon-transitory computer readable media of claim 14, wherein the cRQAparameter between the breathing data and the locomotion data includes amean diagonal line length.
 18. The one or more non-transitory computerreadable media of claim 17, wherein an increased mean diagonal linelength is associated with an estimate of greater severity of COPD. 19.The one or more non-transitory computer readable media of claim 14,wherein the cRQA parameter between the breathing data and the locomotiondata includes an entropy.
 20. The one or more non-transitory computerreadable media of claim 19, wherein an increased entropy is associatedwith an estimate of greater severity of COPD.
 21. The one or morenon-transitory computer readable media of claim 14, wherein the cRQAparameter between the breathing data and the locomotion data includes apercent recurrence.
 22. The one or more non-transitory computer readablemedia of claim 21, wherein a decreased percent recurrence is associatedwith an estimate of greater severity of COPD.
 23. The one or morenon-transitory computer readable media of claim 14, wherein thereference value is a value of the cRQA parameter from a referencepopulation.
 24. The one or more non-transitory computer readable mediaof claim 14, wherein the reference value is a previously obtained valueof the cRQA parameter from the patient.
 25. The one or morenon-transitory computer readable media of claim 14, wherein the estimateof COPD severity in the patient is an indication that the patient's COPDhas increased in severity from a previous time.
 26. The one or morenon-transitory computer readable media of claim 14, wherein the estimateof COPD severity in the patient is an indication that the patient's COPDis elevated with respect to a reference population.
 27. The one or morenon-transitory computer readable media of claim 14, wherein the displayof the estimate of COPD severity in the patient includes an indicator ofan activity level of the patient.
 28. (canceled)
 29. A computing device,comprising: means for receiving breathing data and locomotion data to afourth logic, wherein the breathing data is representative of breathingactivity of the patient over a time interval, and the locomotion data isrepresentative of locomotive activity of the patient over the timeinterval; means for generating an estimate of COPD severity in thepatient by comparing 1) a cross-recurrence quantification analysis(cRQA) parameter between the breathing data and the locomotion data and2) a reference value; and means for providing the estimate of COPDseverity for display.
 30. A method for estimating severity of chronicobstructive pulmonary disease (COPD) in a patient, comprising:receiving, by a computing device, breathing data and locomotion data toa fourth logic, wherein the breathing data is representative ofbreathing activity of the patient over a time interval, and thelocomotion data is representative of locomotive activity of the patientover the time interval; generating, by the computing device, an estimateof COPD severity in the patient by comparing 1) a cross-recurrencequantification analysis (cRQA) parameter between the breathing data andthe locomotion data and 2) a reference value; and providing, by thecomputing device, the estimate of COPD severity for display.